A late fusion approach to combine multiple pedestrian detectors
Pedestrian detection is a well-known problem in Computer Vision. To improve detection, several feature descriptors have been proposed and combined. However, there are cases where the most powerful features fail to discriminate between false positives similar to the human body structure and actual true positives, which is a critical problem for applications such as surveillance, driving assistance and robotics. To address this issue, we propose a novel approach to combine results of distinct pedestrian detectors by reinforcing the human hypothesis. The method is able to reduce the confidence of the false positives due to the lack of spatial consensus when multiple detectors are considered. Our experimental validation, performed on three pedestrian detection benchmarks, INRIA person, ETH and Caltech pedestrian dataset, demonstrates that the proposed approach, referred to as Spatial Consensus (SC), outperforms the state-of-the-art on INRIA and ETH datasets and achieves comparable results on the Caltech dataset.