A Boosted Multi-Task Model for Pedestrian Detection with Occlusion Handling

Abstract

Pedestrian detection is a challenging problem in computer vision. Especially, a major bottleneck for current state-of-the-art methods is the significant performance decline with increasing occlusion. A common technique for occlusion handling is to train a set of occlusion-specific detectors and merge their results directly. These detectors are trained independently and the relationship among them is ignored. In this paper, we consider pedestrian detection in different occlusion levels as different but related problems, and propose a multi-task model to jointly consider their relatedness and differences. The proposed model adopts multi-task learning algorithm to map pedestrians in different occlusion levels to a common space, where all models corresponding to different occlusion levels are constrained to share a common set of features, and a boosted detector is then constructed to distinguish pedestrians from background. The proposed approach is evaluated on the challenging Caltech pedestrian detection benchmark, and achieves state-of-the-art results on different occlusion-specific test sets.