Detection of panoramic vision pedestrian based on deep learning

Abstract

As the AI (artificial intelligence) develops, driverless vehicle technology is widely concerned, and the important problem that needs to be solved in driverless technology is the detection of pedestrians in the panoramic vision of the vehicle, so the pedestrian detection technology of panoramic vision is explored based on the deep learning. Recently, anchor-free and one-stage detectors have been introduced into this area. However, their accuracies are unsatisfactory. Therefore, in order to enjoy the simplicity of anchor-free detectors and the accuracy of two-stage ones simultaneously, some adaptations based on a detector, CSP (center and scale prediction) are proposed. The original CSP of pedestrian detector is improved to make the training process more robust. For example, we use SN layers to replace all BN layers and ResNet-101 is used as backbone based on the research of deep learning. The main contributions of our paper are: (1) we improve the robustness of CSP and make it easier to train. (2) we propose a novel method to predict width, namely compressing width. (3) we achieve the second best performance on CityPersons benchmark, i.e. 9.3% log-average miss rate (MR) on reasonable set, 8.7% MR on partial set and 5.6% MR on bare set, which shows an anchor-free and one-stage detector can still have high accuracy. (4) we explore some capabilities of switchable normalization which are not mentioned in its original paper. This study will provide important theoretical support and practical basis for pedestrian detection