Search

To elude from the camera, suspects often hide behind other things or persons, leading to a series of occlusion patterns. These suspects are notoriously hard to search due to the substantially various appearance in the intricate occlusion patterns. Existing methods solving occlusion problem depend on learning several frequent patterns separately. It brings not only high consumption but also less coverage of patterns in real application scenarios. Different from the current researches which only concern certain patterns that do not synthesize the occlusion patterns in practical applications, we consider a wide range of occlusion patterns which conform the real application scenarios in one coherent model with less interference of both the occlusion and background areas and without redundant computation. Consequently, we propose a channel-guided mechanism (CGM) for occluded suspect search in this paper. The core idea is that different body areas have been activated via different channels in convolutional neural networks. By suppressing the effects of the interference areas, such as occlusion and background areas, we can filter out the visible areas which are the essential elements for the occlusion patterns. Channel-aware attention is introduced to learn the relation between areas and channels. Furthermore, we can identify suspects using a rule which focuses more on the visible area and focuses less on the occluded area in the specific occlusion pattern. Extensive evaluations on two challenging datasets confirm the effectiveness of the proposed CGM. [ABSTRACT FROM AUTHOR]