Latent subcategory models for pedestrian detection with partial occlusion handling

Pedestrian detection is one of the most important tasks in Computer Vision, especially in automotive and security applications. One of the most common problems in real scenarios is related to the detection of occluded pedestrians. In this paper, we propose a novel multi-cue pedestrian detection approach able to deal with non homogeneous object samples by learning latent subcategory models trained on both visual and depth-based features. We also propose a novel self-similarity based feature, namely SST_D, to encode the homogeneity in appearance of pedestrians characterized by similar occlusion patterns. Experiments are performed on the Daimler Pedestrian Detection Benchmark Dataset showing the robustness of our approach in actual scenarios.