Multi-object detection and tracking via kernel covariance factorization in thermal video
Guohua Ren, Ioannis D. Schizas · 2017
This paper addresses the problem of joint object detection and tracking in thermal videos. Object detection is formulated as a sparse factorization task of a properly defined kernel covariance matrix. The support of these estimated factors is used to determine 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. For each object, the centroid pixel 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 in tracking. Numerical tests demonstrate the improved tracking performance over existing alternatives.