Learning to integrate occlusion-specific detectors for heavily occluded pedestrian detection

It is a challenging problem to detect partially occluded pedestrians due to the diversity of occlusion patterns. Although training occlusion-specific detectors can help handle various partial occlusions, it is a nontrivial problem to integrate these detectors properly. A direct combination of all occlusion-specific detectors can be affected by unreliable detectors and usually does not favor heavily occluded pedestrian examples, which can only be recognized by few detectors. Instead of combining all occlusion-specific detectors into a generic detector for all occlusions, we categorize occlusions based on how pedestrian examples are occluded into K groups. Each occlusion group selects its own occlusion-specific detectors and fuses them linearly to obtain a classifer. An L1-norm linear support vector machine (SVM) is adopted to select and fuse occlusion-specific detectors for the K classifiers simultaneously. Thanks to the L1-norm linear SVM, unreliable and irrelevant detectors are removed for each group. Experiments on the Caltech dataset show promising performance of our approach for detecting heavily occluded pedestrians.