Pedestrian Detection Using MB-CSP Model and Boosted Identity Aware Non-Maximum Suppression

Abstract

Pedestrian detection is an important task in autonomous surveillance systems. Despite the rapid progress in pedestrian detection field, detecting occluded pedestrians remains a challenging task due to the great variations in occluded pedestrians appearance and the drastic loss of pedestrian information in some severe cases. In this paper, we tackle the occlusion problem by proposing a multi-branch pedestrian detection model based on center and scale prediction framework. The proposed model employs features extracted from full pedestrian’s body as well as its upper, middle, and lower body parts using four detection branches. This multi-branch approach ensures that data representing the true pedestrian appearances, whether they are partially or completely visible, can dominate the final decision-making, minimizing the interference of non-pedestrian data in the detection. Furthermore, to implement the proposed model, the visibility of different pedestrian parts is appropriately annotated, which facilitates the training process. The final decision is made based on the four MB-CSP branches outputs, using a proposed fusing method, named Boosted Identity Aware-Non Maximum Suppression. On heavy occlusion settings, the proposed model resulted in the miss rates of 27.83%, 47.29% and 33.3% for Caltech-USA, Citypersons and EuroCity Persons datasets, respectively.