Semantic superpixel based vehicle tracking

This paper focuses on tracking multiple vehicles in real-world traffic videos which is very challenging due to frequent interactions and occlusions between different vehicles. To address these problems, we fall back on superpixel which recently has received great attention in a wide range of vision problems, e.g. object segmentation, tracking and recognition, for its ability of capturing local appearance characteristics of objects and their spatial relations. As a mid-level feature, however, superpixel itself is unable to carry semantic information which may restricts their use in these problems. To this end, we introduce semantic information into superpixel from an offline trained semantic object detector and successfully deploy it into the multiple vehicle tracking problem. The benefits of semantic superpixel include: (1) it gains better temporal coherency of superpixel; (2) the effectiveness and robustness of occlusion handling are improved; (3) benefited from semantic analysis, false targets and false trajectories are significantly reduced. Experiments show significant accuracy improvements of our approach in comparison with existing tracking methods.