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

Pedestrian detection is a challenging problem in computer vision, and has achieved impressive progress in recent years. However, the current state-of-the-art methods suffer from significant performance decline with increasing occlusion level of pedestrians. A common approach for occlusion handling is to train a set of occlusion-specific detectors and merge their results directly, but 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 boosted 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 three challenging pedestrian detection data sets, including Caltech, TUD-Brussels, and INRIA, and achieves superior performances against state of the art in the literature on different occlusion-specific test sets.