YANG Zhi-yuan,WU Bin.Minimum barrier distance based tracking via spatio-temporal context learning[J].Optoelectronics Letters,2019,15(1):75-80 Minimum barrier distance based tracking via spatio-temporal context learning Author Name Affiliation YANG Zhi-yuan  The State Key Laboratory of Precision Measuring Technology and Instruments, Tianjin University, Tianjin 300072, China  WU Bin  The State Key Laboratory of Precision Measuring Technology and Instruments, Tianjin University, Tianjin 300072, China  Abstract:       We propose an efficient and robust tracking method based on minimum barrier distance (MBD) and spatio-temporal context (STC) learning. It is robust to noise and blur, and can be evaluated approximately through a Dijkstra-like algorithm, leading to fast computation. We adopt the MBD transform to measure the weights of context pixels, and formulate the spatio-temporal relationship between the object and its surrounding region based on a Bayesian framework to estimate the most likely location of the target. A bounded scale update model is proposed to estimate the size of the object. The whole proposed method runs at nearly 160 frames per second (FPS) on an i5 machine. Extensive experimental results show it has comparable or better comprehensive performance than the original STC and some other leading methods. Hits: 1032 Download times: 0 View Full Text    Download reader