Human tracking in thermal catadioptric omnidirectional vision

We propose to explore a novel tracking system for human tracking in thermal catadioptric omnidirectional (TCO) vision, which is able to realize the surveillance in all-weather and wide field of view conditions. In contrast, previous human tracking system mainly focuses on tracking in conventional imaging system. In this paper, the proposed tracking method adopts the classification posterior probability of Support Vector Machine (SVM) to relate the observation likelihood of particle filter for efficient tracking. However, previous works only employ the final output label of SVM for classification. Due to no existing TCO vision dataset available in public, we establish a dataset including TCO videos and extracted human samples to train the classifier and test the proposed tracking method. Moreover, we adjust tracking window distribution of particle filter to fit the characteristic of catadioptric omnidirectional vision which is the size of target in omni-image depends on the distance between target image and the center of catadioptric omnidirectional image. Finally, the experimental results show that our proposed tracking method has a stable and good performance in TCO vision tracking system.