Region NMS-based deep network for gigapixel level pedestrian detection with two-step cropping

Abstract

Pedestrian detection has emerged as a fundamental technology for autonomous cars, robotics, pedestrian search, and other applications. Although a lot of progress has been made in pedestrian detection, it is still a challenging problem in wide field-of-view surveillance videos due to the random distribution and dynamic characteristics of pedestrians. Especially in large-scale high-resolution images, there are many pedestrians and vast scale variance, so it is difficult to accurately detect all pedestrians. In order to solve this problem, sliding window is used to crop all original images to obtain pre-detection results firstly in this paper. Then, the original images are cropped again with the object as the center utilizing the label files shared in the same scene to get multi-scale images. Finally, a region NMS algorithm, a fusion strategy about the results of small images mapped into large images, is proposed to remove the redundant shredded boxes caused by cropping image. We verify the effectiveness of the proposed method with Faster R-CNN, Cascade R-CNN, IterDet and Scale-aware Fast R-CNN models on PANDA dataset.