Tracking across multiple cameras with overlapping views based on brightness and tangent transfer functions
Chun-Te Chu, Jenq–Neng Hwang, Kung-Ming Lan, Shen-Zheng Wang · 2011
The appearance of one object may be seen differently from distinct cameras with overlapping views due to the color deviation and perspective difference. In this paper, we study these problems and propose an appearance modeling technique in order to perform the tracking across the multiple cameras. For single camera tracking, an effective integrated Kalman filter and multiple kernels tracking scheme is adopted. When maneuvering the tracking across multiple cameras, we build the brightness transfer functions (BTFs) to compensate the color difference between camera views. The BTF is constructed from the overlapping area during tracking by employing robust principal component analysis (RPCA). Moreover, the perspective difference can also be compensated by applying the tangent transfer functions (TTFs) derived by the homography between two cameras. We evaluate the proposed method using several real-scenario videos and obtain the promising results.