Robust tracking of multiple objects in video by adaptive fusion of subband particle filters

作     者：Mahmoud, Ahmed Sherif, Sherif S. 

作者机构：Univ Manitoba Dept Elect & Comp Engn 75 Chancellors Circle Winnipeg MB R3T 5V6 Canada 

出 版 物：《IET COMPUTER VISION》 (IET电脑视觉)

年 卷 期：2018年第12卷第8期

页      面：1207-1218页

核心收录：

学科分类：0808[工学-电气工程] 08[工学] 0812[工学-计算机科学与技术（可授工学、理学学位）] 

主　　题：image fusion video signal processing image sequences object tracking particle filtering (numerical methods) set theory robust multiple object tracking adaptive subband particle filter fusion moving object tracking video sequences background motion shadows partial object camouflage low signal-to-noise ratio robust multiscale visual tracker captured video frame independent particle filters wavelet subband subset 

摘      要：Tracking of moving objects in video sequences is an important research problem because of its many industrial, biomedical, and security applications. Significant progress has been made on this topic in the last few decades. However, the ability to track objects accurately in video sequences that have challenging conditions and unexpected events, e.g. background motion and shadows;objects with different sizes and contrasts;a sudden change in illumination;partial object camouflage;and low signal-to-noise ratio, remains an important research problem. To address such difficulties, the authors developed a robust multiscale visual tracker that represents a captured video frame as different subbands in the wavelet domain. It then applies N independent particle filters to a small subset of these subbands, where the choice of this subset of wavelet subbands changes with each captured frame. Finally, it fuses the outputs of these N independent particle filters to obtain final position tracks of multiple moving objects in the video sequence. To demonstrate the robustness of their multiscale visual tracker, they applied it to four example videos that exhibit different challenges. Compared to a standard full-resolution particle filter-based tracker and a single wavelet subband (LL)(2)-based tracker, their multiscale tracker demonstrates significantly better tracking performance.