A cascade framework for unoccluded and occluded pedestrian detection

This paper presents a novel pedestrian detection framework for efficient detection of both unoccluded and occluded pedestrians, thereby proposing an efficient technique for pedestrian detection in real-time environment. Our framework consists of two layers of detection, the first layer using full body detectors for accurate detection of unoccluded pedestrians and then a cascaded layer of part based detectors to efficiently detect the occluded pedestrians. The full body detectors based techniques are state-of-the art for unoccluded pedestrian detection and the part based model is a viable choice for partially occluded pedestrian detection. In our part based model, we use six parts; three horizontal parts and three vertical parts thereby creating a model that is robust to varying degrees and types of occlusions. Each detection layer utilizes multiple modalities (cues) namely; intensity, dense stereo and dense flow. The use of part based detectors as the cascaded layer also increases the unoccluded pedestrian detection rate by correctly detecting the pedestrians that had been misclassified by the first layer. Thus, the second layer of part based detectors has a synergic effect on the first layer.