Multi-label Learning of Part Detectors for Heavily Occluded Pedestrian Detection
Detecting pedestrians that are partially occluded remains a challenging problem due to variations and uncertainties of partial occlusion patterns. Following a commonly used framework of handling partial occlusions by part detection, we propose a multi-label learning approach to jointly learn part detectors to capture partial occlusion patterns. The part detectors share a set of decision trees via boosting to exploit part correlations and also reduce the computational cost of applying these part detectors. The learned decision trees capture the overall distribution of all the parts. When used as a pedestrian detector individually, our part detectors learned jointly show better performance than their counterparts learned separately in different occlusion situations. The learned part detectors can be further integrated to better detect partially occluded pedestrians. Experiments on the Caltech dataset show state-of-the-art performance of our approach for detecting heavily occluded pedestrians.