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Regularized kernel matrix decomposition for thermal video multi-object detection and tracking

1 Department of Electrical Engineering, University of Texas at Arlington, Arlington, TX, 76019, USA
2 Department of Mathematics, University of Tennessee at Knoxville, TN, 37996, USA

This paper derives a novel algorithm for joint detection and tracking of multiple moving objects in thermal videos. The problem of determining multiple objects in a frame sequence is formulated as the task of factorizing a properly defined kernel covariance matrix into sparse factors. The support of these factors will point to the indices of the pixels that form each object. A coordinate descent approach is utilized to determine the sparse factors, and extract the object pixels. A centroid pixel is estimated for each object which is subsequently tracked via Kalman filtering. A novel interplay between the sparse kernel covariance factorization scheme along with Kalman filtering is proposed to enable joint object detection and tracking, while a divide and conquer strategy is put forth to reduce computational complexity and enable efficient tracking. Extensive numerical tests on both synthetic data and thermal video sequences demonstrate the effectiveness of the novel approach and superior tracking performance compared to existing alternatives.
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Keywords Multi-object tracking; kernel learning; sparse matrix decomposition; Kalman filtering; thermal video

Citation: Ioannis D. Schizas, Vasileios Maroulas, Guohua Ren. Regularized kernel matrix decomposition for thermal video multi-object detection and tracking. Big Data and Information Analytics, 2018, 3(2): 1-23. doi: 10.3934/bdia.2018004


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