This study proposes an efficient method to handle the object occlusions seen in monocular traffic image sequences. The motivation of this study is different methods perform differently in occlusion segmentation and the authors’ idea is to use a situation-driven approach to aggregate different methods in order to get a good performance. This study classifies occlusion into four categories according to the foreground situation and a multilevel occlusion handling framework is utilised. First, the image segmentation algorithm based on convex hull analysis is utilised for intra-frame level occlusion segmentation. The segmentation algorithm is established by the compactness ratio and interior distance ratio of the foreground. Second, an online sample-based classification algorithm is utilised for tracking level occlusion segmentation. Training samples are extracted from the historical frames before occlusion and testing samples are extracted from the current frame by an adaptive searching strategy. The segmentation of occlusion is transferred into the online classification of testing samples. Such algorithm is established by the similarity and coherence of target's property between continuous frames. Experiments on video sequences illustrate the good performance of the proposed method under different conditions with low computational cost.
['Yingfeng Cai', 'Hai Wang', 'Xiaobo Chen', 'Long Chen']
Multilevel framework to handle object occlusions for real-time tracking