
Sign language is regularly adopted by speech-impaired or deaf individuals to convey information; however, it necessitates substantial exertion to acquire either complete knowledge or skill. Sign language recognition (SLR) has the intention to close the gap between the users and the non-users of sign language by identifying signs from video speeches. This is a fundamental but arduous task as sign language is carried out with complex and often fast hand gestures and motions, facial expressions and impressionable body postures. Nevertheless, non-manual features are currently being examined since numerous signs have identical manual components but vary in non-manual components. To this end, we suggest a novel manual and non-manual SLR system (MNM-SLR) using a convolutional neural network (CNN) to get the benefits of multi-cue information towards a significant recognition rate. Specifically, we suggest a model for a deep convolutional, long short-term memory network that simultaneously exploits the non-manual features, which is summarized by utilizing the head pose, as well as a model of the embedded dynamics of manual features. Contrary to other frequent works that focused on depth cameras, multiple camera visuals and electrical gloves, we employed the use of RGB, which allows individuals to communicate with a deaf person through their personal devices. As a result, our framework achieves a high recognition rate with an accuracy of 90.12% on the SIGNUM dataset and 94.87% on RWTH-PHOENIX-Weather 2014 dataset.
Citation: Maher Jebali, Abdesselem Dakhli, Wided Bakari. Deep learning-based sign language recognition system using both manual and non-manual components fusion[J]. AIMS Mathematics, 2024, 9(1): 2105-2122. doi: 10.3934/math.2024105
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Sign language is regularly adopted by speech-impaired or deaf individuals to convey information; however, it necessitates substantial exertion to acquire either complete knowledge or skill. Sign language recognition (SLR) has the intention to close the gap between the users and the non-users of sign language by identifying signs from video speeches. This is a fundamental but arduous task as sign language is carried out with complex and often fast hand gestures and motions, facial expressions and impressionable body postures. Nevertheless, non-manual features are currently being examined since numerous signs have identical manual components but vary in non-manual components. To this end, we suggest a novel manual and non-manual SLR system (MNM-SLR) using a convolutional neural network (CNN) to get the benefits of multi-cue information towards a significant recognition rate. Specifically, we suggest a model for a deep convolutional, long short-term memory network that simultaneously exploits the non-manual features, which is summarized by utilizing the head pose, as well as a model of the embedded dynamics of manual features. Contrary to other frequent works that focused on depth cameras, multiple camera visuals and electrical gloves, we employed the use of RGB, which allows individuals to communicate with a deaf person through their personal devices. As a result, our framework achieves a high recognition rate with an accuracy of 90.12% on the SIGNUM dataset and 94.87% on RWTH-PHOENIX-Weather 2014 dataset.
"A rogue wave" is a single wave that comes out in the ocean "from nowhere" with a much higher amplitude than the average crests around it [1]. It was thought to be mystical until someone had a similar experience [2]. Those who are involved in such a disaster surely will focus on saving lives rather than recording evidence [3]. For such reason, there does not exist a clear understanding of this occurrence until now [4]. The superposition principle based on linear theory is inadequate to explain large amplitude, but nonlinear theory would be more helpful [5]. There are two main characteristics of rogue waves: "appear from nowhere" and "disappear without a trace." The first characteristic lies in modulation instability as the solution encounters abnormal initial input that will increase exponentially [6]. On the other hand, Bespalov-Talanov instability does not last long and will return the solution to its initial state [7]. As a result, in a conservative nonlinear system, each wave that "appears from nowhere" must "disappear without a trace" [8]. In contrast to soliton solution, lump solution is a kind of rational function solution that is localized in each direction [9]. A general rational solution was presented for an integrable system such as the KdV equation, the Boussinesq equation, the Toda lattice equation via the Wronskian and Casoratian determinant method.
Lately, Tian derived lump solution, multi-kink soliton and discussed the interaction between tripe and lump soliton from a (3+1)-dimensional Kadomtsev-Petviashvili equation with the help of Hirota's bilinear method and the Bäcklund transformation [10]. Moreover, the mechanism and transition of the solution have been investigated for the (2+1)-dimensional Sawada-Kotera equation using phase shift analysis and characteristic line [11]. It is worth noting that the ˉ∂-dressing method is proposed by Tian to study the three-component couple Hirota(tcCH) equation [12]. The coupled Maxwell-Bloch equations and the AKNS type integrable shallow water wave equation are studied by Darboux transformation [13,14,15]. Other work that involves Darboux transformation and symmetry condition can be seen in [16]. Recently, the consistent Riccati expansion method and the consistent tanh expansion method are employed to investigate KdV equation [17,18]. Bäcklund-transformation and symbolic computation are also effective in dealing with nonlinear models [19,20,21,22,23,24].
In 1993, Camassa and Holm derived a completely integrable dispersive shallow water wave equation via Hamiltonian methods, i.e.,
ut+2kux−uxxt+auux=2uxuxx+uuxxx, | (1.1) |
where u=u(x,t) is the height of the water's free surface over a flat bottom, related to the spatial coordinate x and time t; k is a constant that has to do with the shallow water wave speed; the subscripts stand for the partial derivatives [25]. As a small amplitude expansion of incompressible Euler's equations for unidirectional wave motion at the free surface under the influence of gravity, the higher order terms (the right-hand side) are usually neglected in the small amplitude, shallow water limit [26]. Leaving those terms brings about the Benjamin-Bona-Mahony equation or the Korteweg-de Vries equation in the same order [27]. Therefore, the Camassa-Holm(CH) equation can be treated as an extension of the Benjamin-Bona-Mahony(BBM) equation [28,29]. This extension renders solitons whose limiting form(k→0) is peak as their first derivative is discontinued(compared with the smooth isolated waves) [30]. Such "peakons" dominate the solutions of the initial value problems under the condition of k=0, i.e.,
u(x,t)=ce−|x−ct|, |
where c is the wave speed, which means u has slope discontinuities [31]. If a derivative discontinuity occurs, we can limit a verticality at each inflection point to break a smooth initial condition into a series of peakons. Moreover, multisolitons can be obtained by superimposing the single solitons and solving as a completely integrable finite dimensional bi-Hamiltonian system, i.e., it can be written in two different kinds of Hamiltonian forms [32]. It is this property modifies the CH equation as a compatibility condition for a linear isospectral problem, such that the inverse scattering transformation can solve the initial value problem. Boyd pointed out why CH equation is important: it is a shallow water model like the KdV equation; two is it gives the peaked periodic waves with discontinuous first derivatives [33]. The higher order terms will come small and disposable under a slowly varying condition ξ=x−ct. Therefore, the soliton is given to the lowest order by the solutions of
ut+2kux−uxxt+auux=0. | (1.2) |
According to Eq (1.2), Wazwaz proposed two modified forms of CH equation
ut+2kux−uxxt+aunux=0, | (1.3) |
and
ut+2kux−uxxt+aun(un)x=0, | (1.4) |
where a>0,k∈R, n is the strength of the nonlinearity [34,35]. Under this sense, Wazwaz further presented two variants of (2+1)-dimensional Camassa-Holm-KP(CH-KP) equations
(ut+2kux−uxxt−aunux)x+uyy=0, | (1.5) |
and
(ut+2kux−uxxt+aun(un)x)x+uyy=0. | (1.6) |
Since Eqs (1.5) and (1.6) are derived from the modified CH equations (1.3) and (1.4), which are similar to the deduction of the Kadomtsev-Petviashvili equation, thus they are called the "Camassa-Holm-KP equation" [36].
With the help of simulation method and dynamical system theory, Xie derived loops, periodic cusp waves, kinks, and peakons of a generalized (2+1)-dimensional CH-KP equation [37]. Biswas obtained analytic 1-soliton solution of two generalized CH-KP equations [38]. Tian investigated breathers, rogue waves and other kinds of solitary waves of a generalized CH-KP equation, through bilinear formalism and homoclinic breather limit approach[39]. Lai studied solitary patterns, periodic and algebraic traveling wave solutions for two generalized (2+1)-dimensional CH-KP equations by direct integration [40]. As for (3+1)-dimensional cases, a generalized (3+1)-dimensional time fractional CH-KP equation was educed in the sense of Riemann-Liouville fractional derivatives with the help of Agrawal's method, Euler-Lagrange equation and semi-inverse method [41].
On the other hand, by adding utt to the KP equation, Wazwaz introduced a generalized KP-Boussinesq equation
uxxxy+3(uxuy)x+utx+uty+utt−uzz=0, |
which makes a significant impact on the phase shift and the dispersion relation [42]. In this paper, following the same manners of Wazwaz, we propose an extension to the CH-KP equation:
(ut+αux+βuux+γuxxt)x+λutt+δuyy=0, | (1.7) |
where u=u(x,y,t) is the height of the water's free surface over a flat bottom; α,β,γ,λ,δ are nonzero constants. Equation (1.7) simulates the dispersion's role in the development of patterns in a liquid drop, and describes left and right traveling waves like the Boussinesq equation.
To our knowledge, the rational and generalized rational solution has not been reported for Eq (1.7) yet. Therefore, in this paper, we intend to find rational and generalized rational solutions of Eq (1.7) with the help of bilinear form and symbolic computation. In Section 2, we derive the second order, third order and fourth order rational solutions and explore the inner connections between the bilinear equation and rational solution by discussing the relevance between the complex roots and the formation of the waves. In Section 3, we obtain generalized rational solutions with two arbitrary parameters, that can be used to modulate the evolution progress. The detailed dynamical behaviors for the generalized rational solutions are discussed in Section 4, including the scatter behavior, exact locations where lump waves space, and the moving path. In Section 5, we analyze the multiple dark wave solitons. Section 6 contains the summary of this paper.
In this section, we will construct rational solutions of Eq (1.7) and assess the inner link between the bilinear equation and rational solution. Taking ξ=x+t, Eq (1.7) will be translated to
(α+λ+1)uξξ+β(u2ξ+uuξξ)+γuξξξξ+δuyy=0, | (2.1) |
where α,λ,β,γ,δ are real parameters. Using the transform u=6γβ(lnf)ξξ, (2.1) becomes
(δD2y+(α+λ+1)D2ξ+γD4ξ)F⋅F=0, |
which is equal to
δ(−2F2y+2FFyy)+(α+λ+1)(−2F2ξ+2FFξξ)+γ(6F2ξξ−8FξFξξξ+2FFξξξξ)=0. | (2.2) |
As we can see, the bilinear equation (2.2) has three parts, and α,λ serve as the same effect in the second part. Therefore, in order to facilitate the following computation, we treat α,λ as the same parameter(setting α+λ=α). Of course, one can retrieves the original result by replacing α with α+λ. With the help of bilinear form (2.2), the rational solution of Eq (1.7) can be obtained by the following theorem.
Theorem 2.1. The generalized CH-KP equation (1.7) has rational solution
un(ξ,y)=6γβ∂2∂ξ2lnFn(ξ,y), | (2.3) |
with
Fn(ξ,y)=n(n+1)/2∑j=0j∑i=0ai,jξ2iy2(j−i), | (2.4) |
where ai,j is real parameter, β,Fn≠0 in order to make un analytic [43].
Using upon procedure one has
F2(ξ,y)=ξ6−25γα+1ξ4−125γ(αγ+γ)(α+1)3ξ2+(α+1)3δ3y6+(3(α2+2α+1)δ2ξ2−17(αγ+γ)δ2)y4+(3(α+1)δξ4−90γδξ2+475γ2(α+1)δ)y2−1875γ3(α+1)3,F3(ξ,y)=ξ12−98γα+1ξ10+735γ2(α+1)2ξ8−75460γ33(α+1)3ξ6−5187875γ43(α+1)4ξ4−159786550γ53(α+1)5ξ2+(α+1)6δ6y12+(6(α+1)5δ5ξ2−58(α+1)4γδ5)y10+(15(α+1)4δ4ξ4−570(α+1)3γδ4ξ2+4335(α+1)2γ2δ4)y8+(20(α+1)3δ3ξ6−1460(α+1)2γδ3ξ4+35420(αγ2+γ2)δ3ξ2−798980γ33δ3)y6+(15(α2+2α+1)δ2ξ8−1540(αγ+γ)δ2ξ6+37450γ2δ2ξ4+14700γ3(α+1)δ2ξ2+16391725γ43(α+1)2δ2)y4+(6(α+1)δξ10−690γδξ8+18620γ2(α+1)δξ6−220500γ3(α+1)2δξ4+565950γ4(α+1)3δξ2−300896750γ53(α+1)4δ)y2+878826025γ69(α+1)6,F4(ξ,y)=ξ20+270γξ18+16605γ2ξ16+351000γ3ξ14−18877950γ4ξ12+2094264900γ5ξ10−178095030750γ6ξ8+6967194507000γ7ξ6+190578711448125γ8ξ4−696163557521250γ9ξ2+y20+(10ξ2+150γ)y18+(45ξ4+2190γξ2+23085γ2)y16+(120ξ6+11400γξ4+354600γ2ξ2+3299400γ3)y14+(210ξ8+31080γξ6+1619100γ2ξ4+35645400γ3ξ2+360709650γ4)y12+(252ξ10+50820γξ8+3601080γ2ξ6+94613400γ3ξ4+671510700γ4ξ2+21813668100γ5)y10+(210ξ12+52500γξ10+4513950γ2ξ8+151237800γ3ξ6+2667498750γ4ξ4+31477666500γ5ξ2+1200881855250γ6)y8+(120ξ14+34440γξ12+3308760γ2ξ10+135286200γ3ξ8+3824793000γ4ξ6+45237339000γ5ξ4+1982064357000γ6ξ2+43199536653000γ7)y6+(45ξ16+13800γξ14+1367100γ2ξ12+56586600γ3ξ10+1071960750γ4ξ8+636363000γ5ξ6−405853402500γ6ξ4+90898176915000γ7ξ2+348683786758125γ8)y4+(10ξ18+3030γξ16+275400γ2ξ14+10621800γ3ξ12+107534700γ4ξ10+4871002500γ5ξ8−521628471000γ6ξ6+33286514625000γ7ξ4+870343420196250γ8ξ2+3474517664913750γ9)y2+5917390238930625γ10. |
Remark 2.1. The solutions plotted in Figure 1 can be thought of as bound states of individual solitons, namely, two-, three- and four-humped multisolitons [44].
We can derive two kinds of waves by choosing different coordinates. For instance, choosing (ξ,y), we can derive multi-lump soliton and (x,t) for multi-wave soliton. Firstly, let us consider the multi-lump soliton. The second order, third order and fourth order rational solutions are illustrated in Figure 1, showing two, three, four lumps under the conditions: α=δ=−γ=1,β=−6 for Figure 1(a) and (b); α=−2,δ=−1,γ=1,β=6 for Figure 1(c). Second order rational solution u2 has two separated peaks while there is one shaper peak in the middle surrounded by two shorter peaks for u3. As for u4, it has four divided peaks, two in the middle and two guarding around. Apparently, we can conclude that the nth order rational solution un has n separated peaks and the maximum value locates at y=0 with n maximum values of un. On the other hand, multi-wave solitons are shown in Figures 2–4. According to the evolutional plots, these waves stem from a plain wave background and hit the maximum amplitude at y=0. Then, they disappear with time. Interestingly, we find that higher order multi-wave solitons may contain lower ones. For example, the two-wave soliton does exist in the evolution progress of the three-wave soliton (see Figure 3(a)) and the three-wave soliton does exist in the evolution progress of the four-wave soliton (see Figure 4(a)).
Remark 2.2. Here, the solitons are plotted in (ξ,t)-plane and (x,t)-plane, and they are evolutionary of variable y. For some other models that transformed by ξ=x+y in (2.1), the corresponding solutions will be evolutionary of t.
In this part, we will look into the connection between polynomial Fn and rational solution un, through discussing the relevance between the complex roots and the formation of the waves. In Figure 5, each picture gives the complex root of Fn=0 on the complex field, for y=0,y=3n(n=2,3,4) of x, i.e.,
{F2(ξ,0)=0F2(ξ,6)=0,{F3(ξ,0)=0F3(ξ,9)=0,{F4(ξ,0)=0F4(ξ,12)=0. |
As shown in Figure 5, the red points are closer to the real axis than the blue points, which means the complex roots of Fn=0 will leave the real axis when y grows. This result also accords with Figure 6. Above pictures demonstrate a "triangular pattern" for y=0,y=3n. For instance, the complex roots of F2(ξ,0)=0 construct two adjacent triangles, and the complex roots of F2(ξ,6)=0 form two divided triangles.
We overlay the sectional view of u4 on the map of the complex roots of F4(ξ,y)=0 in Figure 7. Unlike Figures 5 and 6, we choose six different values of y, that is y=−15,−2,0,1,4,15. As shown in Figure 7, the roots move to the real axis as y increases and y=0 is the moment that those roots cluster together near the base point most closely. Next, they move away from the base point and form as isolated triangles like the initial condition. Together with the sectional view of u4, we come to the conclusion that more complex roots around the base point, higher amplitude the wave has.
Above, we have investigated the rational solutions of Eq (1.7) and assessed the inner link between the bilinear equation and the rational solution. Next, we are going to construct the generalized rational solutions of Eq (1.7) by introducing two free parameters μ,ν, which possess more complex mechanisms than their counterpart. In this case, the generalized rational solution also has two kinds of waves, multi-lump soliton and dark-multi-wave soliton (compare with the bright-multi-wave soliton in Section 2). With the help of bilinear form (2.2), the generalized rational solution of Eq (1.7) can be obtained by the following theorem.
Theorem 3.1. The extended CH-KP equation (1.7) has generalized rational solution
ˉun(ξ,y,μ,ν)=6γβ∂2∂ξ2lnˉFn(ξ,y,μ,ν), | (3.1) |
with
ˉFn+1(ξ,y,μ,ν)=Fn+1(ξ,y)+2μyPn(ξ,y)+2νξQn(ξ,y)+(μ2+ν2)Fn−1(ξ,y), | (3.2) |
and
Pn(ξ,y)=n(n+1)/2∑j=0j∑i=0bi,jξ2iy2(j−i),Qn(ξ,y)=n(n+1)/2∑j=0j∑i=0ci,jξ2iy2(j−i), |
where ˉFn≠0 and μ,ν,bi,j,ci,j are real parameters [43].
Substituting ¯F2 into (2.2) and equating the coefficients of all powers of ξ,y to zero, we have
a0.0=−1875γ3a3.3(α+1)3+δμ2b21.1+9(α+1)ν2c21.19(α+1)a3.3−μ2−ν2,a0.1=475γ2a3.3(α+1)δ,a0.2=−17(α+1)γa3.3δ2,a0.3=(α+1)3a3.3δ3,a1.1=−125γ2a3.3(α+1)2,a1.2=−90γa3.3δ,a1.3=3(α+1)2a3.3δ2,a2.2=−25γa3.3α+1,a2.3=3(α+1)a3.3δ,b0.0=−5γb1.13(α+1),b0.1=−(α+1)b1.13δ,c0.0=γc1.1α+1,c0.1=−3(α+1)c1.1δ, | (3.3) |
where a3.3,b1.1,c1.1 are arbitrary constants. Substituting into (3.1) and using u=6γβ(lnf)ξξ, the solution of Eq (1.7) can be written as
ˉu2=6γβ(lnˉF2(ξ,y,μ,ν))ξξ. |
Here, α=−γ=σ=a3.3=b1.1=c1.1=1,β=−6. Starting from the rational soliton u2(ξ,y) (see Figure 1(a)), the evolution plots of the generalized rational soliton ˉu2(ξ,y,μ,ν) are displayed in Figure 8. Numerical simulation indicates that there are two peaks for ˉu2(ξ,y,μ,ν) when μ,ν are small. One of the peaks shrinks, and divides into two small hills as μ,ν increase. After that, those tiny hills will rise up if μ,ν continue increase. Eventually ˉu2(ξ,y,μ,ν) contains three separated lump waves and organizes a triangle (see Figure 9).
Substituting ¯F3 into (2.2) and equating the coefficients of all powers of ξ,y to zero, we have
a0.0=878826025(α+1)2γ6−27γ((α+1)7ν2+δ7μ2)9(α+1)8(μ2+ν2+1),a0.2=16391725γ43(α+1)2δ2,a0.3=−798980γ33δ3,a0.1=3((α+1)7ν2+δ7μ2)−300896750(α+1)2γ53(α+1)6δ(μ2+ν2+1),a0.4=4335(α+1)2γ2δ4,a0.5=−58(α+1)4γδ5,a1.1=3((α+1)7ν2+δ7μ2)−159786550(α+1)2γ53(α+1)7(μ2+ν2+1),a0.6=(α+1)6δ6,a1.2=565950γ4(α+1)3δ,a1.3=14700γ3(α+1)δ2,a1.4=35420(αγ2+γ2)δ3,a1.5=−570(α+1)3γδ4,a1.6=6(α+1)5δ5,a2.2=−5187875γ43(α+1)4,a2.3=−220500γ3(α+1)2δ,a2.4=37450γ2δ2,a2.5=−1460(α+1)2γδ3,a2.6=15(α+1)4δ4,a3.3=−75460γ33(α+1)3,a3.4=18620γ2(α+1)δ,a3.5=−1540(αγ+γ)δ2,a3.6=20(α+1)3δ3,a4.4=735γ2(α+1)2,a4.5=−690γδ,a4.6=15(α2+2α+1)δ2,a5.5=−98γα+1,a5.6=6(α+1)δ,b0.0=−18865γ3δ33(α+1)6,b0.1=−245γ2δ2(α+1)4,b0.2=7γδ(α+1)2,b1.1=−665γ2δ3(α+1)5,b1.2=190γδ2(α+1)3,b1.3=−9δα+1,b2.2=−105γδ3(α+1)4,b2.3=−5δ2(α+1)2,b3.3=5δ3(α+1)3,c0.0=−12005γ33(α+1)3, |
c0.1=535γ2(α+1)δ,c0.2=−45(αγ+γ)δ2,c0.3=5(α+1)3δ3,c1.1=−245γ2(α+1)2,c1.2=230γδ,c1.3=−5(α2+2α+1)δ2,c2.2=−13γα+1,c2.3=−9(α+1)δ. |
Substituting into (3.1) and using u=6γβ(lnf)ξξ, the solution of Eq (1.7) can be written as
ˉu3=6γβ(lnˉF3(ξ,y,μ,ν))ξξ. |
Here, α=−γ=σ=1,β=−6. From Figure 1(b), we know that the rational solution u3(ξ,y) only has three peaks, with one in the middle and two guarding around. As μ,ν increases, two bilateral peaks start to split. To be specific, the left side peak divides into three, while the right side peak divides into two(see Figure 10). For sufficient large μ,ν, ˉu3(ξ,y,μ,ν) organizes a pentagon with one peak in the middle and five around it(see Figure 11).
Substituting ¯F4 into (2.2) and equating the coefficients of all powers of ξ,y to zero, we have
a9.9=270γ,a9.10=10,a8.8=16605γ2,a8.9=3030γ,a8.10=45,a7.7=351000γ3,a7.8=275400γ2,a7.9=13800γ,a7.10=120,a6.6=−18877950γ4,a6.7=10621800γ3,a6.8=1367100γ2,a6.9=34440γ,a6.10=210,a5.5=2094264900γ5,a5.6=107534700γ4,a5.7=56586600γ3,a5.8=3308760γ2,a5.9=52500γ,a5.10=252,a4.4=−178095030750γ6,a4.5=4871002500γ5,a4.6=1071960750γ4,a4.7=135286200γ3,a4.8=4513950γ2,a4.9=50820γ,a4.10=210,a3.3=6967194507000γ7,a3.4=−521628471000γ6,a3.5=636363000γ5,a3.6=3824793000γ4,a3.7=151237800γ3,a3.8=3601080γ2,a3.9=31080γ,a3.10=120,a2.2=190578711448125γ8,a2.3=33286514625000γ7,a2.4=−405853402500γ6,a2.5=45237339000γ5,a2.6=2667498750γ4,a2.7=94613400γ3,a2.8=1619100γ2,a2.9=11400γ,a2.10=45,a1.1=−696163557521250γ9,a1.2=870343420196250γ8,a1.3=90898176915000γ7,a1.4=1982064357000γ6,a1.5=31477666500γ5,a1.6=671510700γ4,a1.7=35645400γ3,a1.8=354600γ2,a1.9=2190γ,a1.10=10,a0.0=5917390238930625γ10,a0.1=3474517664913750γ9,a0.2=348683786758125γ8,a0.3=43199536653000γ7,a0.4=1200881855250γ6,a0.5=21813668100γ5,a0.6=360709650γ4,a0.7=3299400γ3,a0.8=23085γ2,a0.9=150γ,b0.6=√−c24.6ν2+625μ2+625ν225μ,b0.5=2γ√−c24.6ν2+625μ2+625ν25μ,b1.6=−18√−c24.6ν2+625μ2+625ν225μ,b0.4=−9γ2√−c24.6ν2+625μ2+625ν2μ,b1.5=−262γ√−c24.6ν2+625μ2+625ν25μ,b2.6=−√−c24.6ν2+625μ2+625ν2μ,b0.3=684γ3√−c24.6ν2+625μ2+625ν2μ,b1.4=−15684γ2√−c24.6ν2+625μ2+625ν25μ,b2.5=−156γ√−c24.6ν2+625μ2+625ν25μ,b3.6=36√−c24.6ν2+625μ2+625ν225μ,b0.2=443079γ4√−c24.6ν2+625μ2+625ν2μ,b1.3=−52668γ3√−c24.6ν2+625μ2+625ν2μ, |
b2.4=4746γ2√−c24.6ν2+625μ2+625ν2μ,b3.5=6132γ√−c24.6ν2+625μ2+625ν225μ,b4.6=63√−c24.6ν2+625μ2+625ν225μ,b0.1=6707610γ5√−c24.6ν2+625μ2+625ν2μ,b1.2=−660618γ4√−c24.6ν2+625μ2+625ν2μ,b2.3=33012γ3√−c24.6ν2+625μ2+625ν2μ,b5.6=14√−c24.6ν2+625μ2+625ν225μ,b0.0=−11764935γ6√−c24.6ν2+625μ2+625ν2μ,b1.1=−11755926γ5√−c24.6ν2+625μ2+625ν2μ,b2.2=384111γ4√−c24.6ν2+625μ2+625ν2μ,b3.3=−5796γ3√−c24.6ν2+625μ2+625ν2μ,b4.4=−105γ2√−c24.6ν2+625μ2+625ν2μ,b5.5=−126γ√−c24.6ν2+625μ2+625ν25μ,b6.6=−7√−c24.6ν2+625μ2+625ν225μ,c6.6=−c4.625,c5.5=−7425γc4.6,c5.6=18c4.625,c4.4=33γ2c4.6,c4.5=3745γc4.6,c3.3=−1692γ3c4.6,c3.4=53165γ2c4.6,c3.5=3485γc4.6,c3.6=−36c4.625,c2.2=89145γ4c4.6,c2.3=13356γ3c4.6,c2.4=294γ2c4.6,c2.5=−10925γc4.6,c2.6=−63c4.625,c1.1=−3130218γ5c4.6,c1.2=−675990γ4c4.6,c1.3=−52164γ3c4.6,c1.4=−5964γ2c4.6,c1.5=−3785γc4.6,c1.6=−14c4.625,c0.0=1799343γ6c4.6,c0.1=15804054γ5c4.6,c0.2=758961γ4c4.6,c0.3=24948γ3c4.6,c0.4=609γ2c4.6,c0.5=29425γc4.6,c0.6=7c4.625. |
where c4,6 is an arbitrary constant. Substituting into (3.1) and using u=6γβ(lnf)ξξ, the solution of Eq (1.7) can be written as
ˉu4=6γβ(lnˉF4(ξ,y,μ,ν))ξξ. |
Here, α=−2,β=6,γ=1,σ=−1. The evolution of ˉu4(ξ,y,μ,ν) is a bit more complicated than the lower order ones. In Figure 12, two guarding peaks divide into four separated peaks, as another three tiny hills rise nearby the central peaks. For sufficient large μ,ν, ˉu4(ξ,y,μ,ν) possesses one double-peak wave in the middle and seven in a ring around it, composing a heptagram(see Figure 13).
Remark 3.1. The multi-breather soliton that has similar structure such as forming a triangle, a pentagon and a heptagram can be seen in a generalized KP-BBM equation [45].
Next, we will study the scatter behavior of the multi-lump soliton by selecting different μ,ν. Based on previous discussion, we know that ˉun is capable of scattering to three-, six- and eight-lump waves as μ,ν increases. It is natural to wonder, what will happen if these parameters continue to increase? Therefore, we choose three different values of μ,ν for each solution to analyze their behavior. According to Figures 14–16, ˉu2,ˉu3,ˉu4 own homologous character: if μ,ν continue to rise, the lumps will maintain their shape and expand the clearance to a larger picture.
According to above analysis, we know that if μ,ν continues to increase, these separated lumps will keep their formation and extend over a wider space. Obviously, the propagation observes a specific pattern. For this purpose, we will calculate the exact locations where extremum are and discuss their features. Under the same parametric conditions in Section 3, solve ∂ˉun/∂ξ=0,∂ˉun/∂y=0 and omit extra point, we can obtain the location of extremum. Consequently, one has the following three circumcircles (see Figure 17)
(ξ−0.029)2+(y+0.038)2=762.511,(ξ+0.030)2+(y−0.122)2=837.548,(ξ−0.052)2+(y+0.191)2=946.623, |
where the lump waves locate, for ˉu2(ξ,y,104,104),ˉu3(ξ,y,107,107),ˉu4(ξ,y,1010,1010), severally. It is noteworthy that during our calculation, the selection of the point is processed in a random order, and we find that other extremum accords with the same path.
We select several alternative values of μ,ν for each circumstance and overlay them in one picture. In Figure 18(a), we set μ=ν=6×104,2×105,5×105,1×106. In Figure 18(b), we set μ=ν=1×107,1×108,5×108,1.6×109. In Figure 18(c), we set μ=ν=5×1010,4×1011,2×1012,7×1012. Based on above superimposed contour plots, as μ,ν grows, the space between each other turns bigger, and the lump waves all move along a straight line in three, five, or seven different paths (red lines), respectively. Furthermore, except for ˉu2, the higher order generalized rational solution has one stable solitary wave in the middle of the picture. Therefore, we have answered the query proposed at the beginning of Section 4: "what will happen if these parameters continue to increase?".
For the generalized rational solution ˉun, it also has two appearances: multi-lump and multi-wave. Above, we have established the multi-lump soliton and studied its mechanism. Further, we will analyze the dark-multi-waves. Different from the bright-multi-waves obtained in Section 2, the dark-multi-waves do not come from a plain wave background and do not disappear with time. In Figure 19, there is one stripe soliton hidden in the valley at the initial input y=−0.2. It starts to shrink at first, then rises up and forms two gullies. As for the second order generalized rational solution ˉu2, there are three stripe solitons in the valley at the initial datum. Then, these stripe solitons converge into one and rise up above the surface (see Figure 20). The evolution of the third order generalized rational solution ˉu3 is more complicated than the others. As is shown in Figure 21, there are three hidden stripe solitons in the middle at the beginning. Then, they converge into two and divide into three and finally leave us four gullies.
This paper proposes a new extension to the Camassa-Holm-Kadomtsev-Petviashvili (CH-KP) equation by adding an additional term utt, which simulates the dispersion's role in the development of patterns in a liquid drop and describes left and right traveling waves like the Boussinesq equation. We have derived the rational and generalized rational solutions for this equation through its bilinear form and symbolic computation. By plotting them in different planes, two waveforms exist: multi-lump and multi-wave. Two-, three- and four-lump solitons are demonstrated in Figure 1. Except for the multi-lump soliton, we have plotted the multi-wave solitons in Figures 2–4, showing as multiple order line rogue waves. According to our numerical simulations, these waves stem from a plain wave background and hit the maximum amplitude at y=0. Then, they disappear with time. Furthermore, we have explored the inner link between Fn and un by discussing the relevance between complex roots of Fn=0 and the formation of waves. As for the generalized rational solution ˉun with two free parameters μ,ν, we have systematically analyzed its scatter behavior by setting different μ,ν. The multiple order dark waves are introduced in Section 5, and they are different from the multi-wave solitons in Section 2. We believe these results may aid in explaining the progress of rogue waves, as they not only provide a complete picture of how a rogue wave can "come out of nowhere and disappear without a trace", but also makes it realistic in the actual world. Moreover, multi-component and higher order rational solutions demonstrating diverse interesting phenomena, particularly in fully discrete and (3+1)-dimensional cases, would be very interesting topics in future research.
The authors declare that they have no conflict of interest.
[1] |
W. C. Stokoe, Sign language structure, Annu. Rev. Anthropol., 9 (1980), 365–390. http://dx.doi.org/10.1146/annurev.an.09.100180.002053 doi: 10.1146/annurev.an.09.100180.002053
![]() |
[2] | J. Napier, L. Leeson, Sign language in action, London: Palgrave Macmillan, 2016. http://dx.doi.org/10.1057/9781137309778 |
[3] |
D. Lowe, Object recognition from local scale-invariant features, Proc. IEEE Int. Conf. Comput. Vision, 2 (1999), 1150–1157. http://dx.doi.org/10.1109/ICCV.1999.790410 doi: 10.1109/ICCV.1999.790410
![]() |
[4] |
Q. Zhu, M. C. Yeh, K. T. Cheng, S. Avidan, Fast human detection using a cascade of histograms of oriented gradients, Proc. IEEE Comput. Soc. Conf. Comput. Vision Pattern Recogn., 2006, 1491–1498. http://dx.doi.org/10.1109/CVPR.2006.119 doi: 10.1109/CVPR.2006.119
![]() |
[5] |
A. Memiş, S. Albayrak, A Kinect based sign language recognition system using spatio-temporal features, Proc. SPIE Int. Soc. Opt. Eng., 9067 (2013), 179–183. http://dx.doi.org/10.1117/12.2051018 doi: 10.1117/12.2051018
![]() |
[6] |
O. Sincan, H. Keles, Using motion history images with 3D convolutional networks in isolated sign language recognition, IEEE Access, 10 (2022), 18608–18618. http://dx.doi.org/10.1109/ACCESS.2022.3151362 doi: 10.1109/ACCESS.2022.3151362
![]() |
[7] |
G. Castro, R. R. Guerra, F. G. Guimarães, Automatic translation of sign language with multi-stream 3D CNN and generation of artificial depth maps, Expert Syst. Appl., 215 (2023), 119394. http://dx.doi.org/10.1016/j.eswa.2022.119394 doi: 10.1016/j.eswa.2022.119394
![]() |
[8] |
J. Huang, W. G. Zhou, H. G. Li, W. P. Li, Attention-based 3D-CNNs for large-vocabulary sign language recognition, IEEE T. Circ. Syst. Vid., 9 (2018), 2822–2832. http://dx.doi.org/10.1109/TCSVT.2018.2870740 doi: 10.1109/TCSVT.2018.2870740
![]() |
[9] |
K. Lim, A. Tan, C. P. Lee, S. Tan, Isolated sign language recognition using convolutional neural network hand modelling and hand energy image, Multimed. Tools Appl., 78 (2019), 19917–19944. http://dx.doi.org/10.1007/s11042-019-7263-7 doi: 10.1007/s11042-019-7263-7
![]() |
[10] |
M. Terreran, M. Lazzaretto, S. Ghidoni, Skeleton-based action and gesture recognition for human-robot collaboration, Intell. Auton. Syst., 577 (2022), 29–45. http://dx.doi.org/10.1007/978-3-031-22216-0_3 doi: 10.1007/978-3-031-22216-0_3
![]() |
[11] |
L. Roda-Sanchez, C. Garrido-Hidalgo, A. S. García, T. Olivares, A. Fernández-Caballero, Comparison of RGB-D and IMU-based gesture recognition for human-robot interaction in remanufacturing, Int. J. Adv. Manuf. Technol., 124 (2023), 3099–3111. http://dx.doi.org/10.1007/s00170-021-08125-9 doi: 10.1007/s00170-021-08125-9
![]() |
[12] |
W. Aditya, T. K. Shih, T. Thaipisutikul, A. S. Fitriajie, M. Gochoo, F. Utaminingrum, et al., Novel spatio-temporal continuous sign language recognition using an attentive multi-feature network, Sensors, 22 (2022), 6452. http://dx.doi.org/10.3390/s22176452 doi: 10.3390/s22176452
![]() |
[13] |
H. Liu, H. Nie, Z. Zhang, Y. F. Li, Anisotropic angle distribution learning for head pose estimation and attention understanding in human-computer interaction, Neurocomputing, 433 (2020), 310–322. http://dx.doi.org/10.1016/j.neucom.2020.09.068 doi: 10.1016/j.neucom.2020.09.068
![]() |
[14] |
S. Sharma, R. Gupta, A. Kumar, Continuous sign language recognition using isolated signs data and deep transfer learning, J. Amb. Intel. Hum. Comp., 2021, 1–12. http://dx.doi.org/10.1007/s12652-021-03418-z doi: 10.1007/s12652-021-03418-z
![]() |
[15] |
O. Koller, S. Zargaran, H. Ney, R. Bowden, Deep sign: Enabling robust statistical continuous sign language recognition via hybrid CNN-HMMs, Int. J. Comput. Vision, 126 (2018), 1311–1325. http://dx.doi.org/10.1007/s11263-018-1121-3 doi: 10.1007/s11263-018-1121-3
![]() |
[16] |
O. Koller, H. Ney, R. Bowden, Deep hand: How to train a CNN on 1 million hand images when your data is continuous and weakly labelled, IEEE Conf. Comput. Vision Pattern Recogn., 2016, 3793–3802. http://dx.doi.org/10.1109/CVPR.2016.412 doi: 10.1109/CVPR.2016.412
![]() |
[17] | O. Koller, S. Zargaran, H. Ney, R. Bowden, Deep sign: Hybrid CNN-HMM for continuous sign language recognition, Brit. Conf. Mach. Vision, 2016. |
[18] |
O. Koller, H. Ney, R. Bowden, Re-sign: Re-aligned end-to-end sequence modelling with deep recurrent CNN-HMMs, IEEE Conf. Comput. Vision Pattern Recogn., 2017, 4297–4305. http://dx.doi.org/10.1109/CVPR.2017.364 doi: 10.1109/CVPR.2017.364
![]() |
[19] |
O. Özdemir, İ. Baytaş, L. Akarun, Multi-cue temporal modeling for skeleton-based sign language recognition, Front. Neurosci., 17 (2023), 1148191. http://dx.doi.org/10.3389/fnins.2023.1148191 doi: 10.3389/fnins.2023.1148191
![]() |
[20] |
H. Butt, M. R. Raza, M. R. Ramzan, M. J. Ali, M. Haris, Attention-based CNN-RNN Arabic text recognition from natural scene images, Forecasting, 3 (2021), 520–540. http://dx.doi.org/10.3390/forecast3030033 doi: 10.3390/forecast3030033
![]() |
[21] |
P. P. Roy, P. Kumar, B. G. Kim, An efficient sign language recognition (SLR) system using camshift tracker and hidden markov model (HMM), SN Comput. Sci., 2 (2021), 1–15. http://dx.doi.org/10.1007/s42979-021-00485-z doi: 10.1007/s42979-021-00485-z
![]() |
[22] |
L. Pigou, A. Oord, S. Dieleman, M. V. Herreweghe, J. Dambre, Beyond temporal pooling: Recurrence and temporal convolutions for gesture recognition in video, Int. J. Comput. Vision, 126 (2018), 430–439. http://dx.doi.org/10.1007/s11263-016-0957-7 doi: 10.1007/s11263-016-0957-7
![]() |
[23] |
J. Huang, W. G. Zhou, Q. L. Zhang, H. Q. Li, W. P. Li, Video-based sign language recognition without temporal segmentation, Proc. AAAI Conf. Artif. Intell., 32 (2018). http://dx.doi.org/10.1609/aaai.v32i1.11903 doi: 10.1609/aaai.v32i1.11903
![]() |
[24] |
K. Han, X. Y. Li, Research method of discontinuous-gait image recognition based on human skeleton keypoint extraction, Sensors, 23 (2023), 7274. http://dx.doi.org/10.3390/s23167274 doi: 10.3390/s23167274
![]() |
[25] |
D. Wategaonkar, R. Pawar, P. Jadhav, T. Patole, R. Jadhav, S. Gupta, Sign gesture interpreter for better communication between a normal and deaf person, J. Pharm. Negat. Result., 2022, 5990–6000. http://dx.doi.org/10.47750/pnr.2022.13.S07.731 doi: 10.47750/pnr.2022.13.S07.731
![]() |
[26] |
M. Jebali, A. Dakhli, M. Jemni, Vision-based continuous sign language recognition using multimodal sensor fusion, Evol. Syst., 12 (2021), 1031–1044. http://dx.doi.org/10.1007/s12530-020-09365-y doi: 10.1007/s12530-020-09365-y
![]() |
[27] |
M. Jebali, A. Dakhli, W. Bakari, Deep learning-based sign language recognition system for cognitive development, Cogn. Comput., 2023, 1–13. http://dx.doi.org/10.1007/s12559-023-10182-z doi: 10.1007/s12559-023-10182-z
![]() |
[28] |
V. Choutas, P. Weinzaepfel, J. Revaud, C. Schmid, PoTion: Pose motion representation for action recognition, Proc. IEEE Conf. Comput. Vision Pattern Recogn., 2018, 7024–7033. http://dx.doi.org/10.1109/CVPR.2018.00734 doi: 10.1109/CVPR.2018.00734
![]() |
[29] |
S. Yan, Y. Xiong, D. Lin, Spatial temporal graph convolutional networks for skeleton-based action recognition, Proc. AAAI Conf. Artif. Intell., 32 (2018). http://dx.doi.org/10.1609/aaai.v32i1.12328 doi: 10.1609/aaai.v32i1.12328
![]() |
[30] |
M. Bicego, M. Vázquez-Enríquez, J. L. Alba-Castro, Active class selection for dataset acquisition in sign language recognition, Image Anal. Proc., 2023,303–315. http://dx.doi.org/10.1007/978-3-031-43148-7_26 doi: 10.1007/978-3-031-43148-7_26
![]() |
[31] |
M. Li, S. Chen, X. Chen, Y. Zhang, Y. Wang, Q. Tian, Actional-structural graph convolutional networks for skeleton-based action recognition, IEEE Conf. Comput. Vision Pattern Recogn., 2019, 3590–3598. http://dx.doi.org/10.1109/CVPR.2019.00371 doi: 10.1109/CVPR.2019.00371
![]() |
[32] |
Y. F. Song, Z. Zhang, C. Shan, L. Wang, Constructing stronger and faster baselines for skeleton-based action recognition, IEEE T. Pattern Anal., 45 (2022), 1474–1488. http://dx.doi.org/10.1109/TPAMI.2022.3157033 doi: 10.1109/TPAMI.2022.3157033
![]() |
[33] |
Z. Wu, C. Shen, A. Hengel, Wider or deeper: Revisiting the ResNet model for visual recognition, Pattern Recogn., 90 (2019), 119–133. http://dx.doi.org/10.1016/j.patcog.2019.01.006 doi: 10.1016/j.patcog.2019.01.006
![]() |
[34] |
N. Takayama, G. Benitez-Garcia, H. Takahashi, Masked batch normalization to improve tracking-based sign language recognition using graph convolutional networks, IEEE Int. Conf. Autom. Face Gesture Recogn., 2021, 1–5. http://dx.doi.org/10.1109/FG52635.2021.9667007 doi: 10.1109/FG52635.2021.9667007
![]() |
[35] |
Ç. Gökçe, Ç. Özdemir, A. A. Kındıroğlu, L. Akarun, Score-level multi cue fusion for sign language recognition, Eur. Conf. Comput. Vision, 2020,294–309. http://dx.doi.org/10.48550/arXiv.2009.14139 doi: 10.48550/arXiv.2009.14139
![]() |
[36] |
L. Tarrés, G. I. Gállego, A. Duarte, J. Torres, X. Giró-i-Nieto, Sign language translation from instructional videos, IEEE Conf. Comput. Vision Pattern Recogn. Work., 2023, 5625–5635. http://dx.doi.org/10.1109/CVPRW59228.2023.00596 doi: 10.1109/CVPRW59228.2023.00596
![]() |
[37] |
O. Sincan, A. Tur, H. Keles, Isolated sign language recognition with multi-scale features using LSTM, Proc. Commun. Appl. Conf., 2019, 1–4. http://dx.doi.org/10.1109/SIU.2019.8806467 doi: 10.1109/SIU.2019.8806467
![]() |
[38] |
Q. Guo, S. J. Zhang, L. W. Tan, K. Fang, Y. H. Du, Interactive attention and improved GCN for continuous sign language recognition, Biomed. Signal Proces., 85 (2023), 104931. http://dx.doi.org/10.1016/j.bspc.2023.104931 doi: 10.1016/j.bspc.2023.104931
![]() |
[39] |
Z. Niu, B. Mak, Stochastic fine-grained labeling of multi-state sign glosses for continuous sign language recognition, Eur. Conf. Comput. Vision, 2020,172–186. http://dx.doi.org/10.1007/978-3-030-58517-4_11 doi: 10.1007/978-3-030-58517-4_11
![]() |
[40] |
A. Hao, Y. Min, X. Chen, Self-mutual distillation learning for continuous sign language recognition, Int. Conf. Comput. Vision, 2021, 11303–11312. http://dx.doi.org/10.1109/ICCV48922.2021.01111 doi: 10.1109/ICCV48922.2021.01111
![]() |
[41] |
D. Guo, S. Wang, Q. Tian, M. Wang, Dense temporal convolution network for sign language translation, Int. Joint Conf. Artif. Intell., 2019,744–750. http://dx.doi.org/10.24963/ijcai.2019/105 doi: 10.24963/ijcai.2019/105
![]() |
[42] |
D. Guo, S. G. Tang, M. Wang, Connectionist temporal modeling of video and language: A joint model for translation and sign labeling, Int. Joint Conf. Artif. Intell., 2019,751–757. http://dx.doi.org/10.24963/ijcai.2019/106 doi: 10.24963/ijcai.2019/106
![]() |
[43] |
I. Papastratis, K. Dimitropoulos, D. Konstantinidis, P. Daras, Continuous sign language recognition through cross-modal alignment of video and text embeddings in a joint-latent space, IEEE Access, 8 (2020), 91170–91180. http://dx.doi.org/10.1109/ACCESS.2020.2993650 doi: 10.1109/ACCESS.2020.2993650
![]() |
[44] |
M. Parelli, K. Papadimitriou, G. Potamianos, G. Pavlakos, P. Maragos, Spatio-temporal graph convolutional networks for continuous sign language recognition, IEEE Int. Conf. Acous. Speech Signal Proc., 2022, 8457–8461. http://dx.doi.org/10.1109/ICASSP43922.2022.9746971 doi: 10.1109/ICASSP43922.2022.9746971
![]() |
[45] |
R. Li, L. Meng, Multi-view spatial-temporal network for continuous sign language recognition, Comput. Vision Pattern Recogn, 2022. http://dx.doi.org/10.48550/arXiv.2204.08747 doi: 10.48550/arXiv.2204.08747
![]() |
[46] |
Z. C. Cui, W. B. Zhang, Z. X. Li, Z. Q. Wang, Spatial-temporal transformer for end-to-end sign language recognition, Complex Intell. Syst., 9 (2023), 4645–4656. http://dx.doi.org/10.1007/s40747-023-00977-w doi: 10.1007/s40747-023-00977-w
![]() |
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