Pedestrian as Points: An Improved Anchor-Free Method for Center-Based Pedestrian Detection

IEEE Access

Koji Kotani¹  Qiu Chen²  Shuai Yang³  Jiawei Cai³  Feifei Lee³  Hanqing Chen³  Chaowei Lin³ 

[1] Department of Intelligent Mechatronics, Akita Prefectural University, Akita, Japan;Major of Electrical Engineering and Electronics, Graduate School of Engineering, Kogakuin University, Tokyo, Japan;School of Optical-Electrical and Computer Engineering, University of Shanghai for Science and Technology, Shanghai, China;

关键词: Pedestrian detection;    anchor-free;    CNNs;    feature pyramid network;    deep semantic information;   

DOI  :  10.1109/ACCESS.2020.3027590

来源: DOAJ

【 摘 要 】

Although excessive proposals using traditional sliding-window methods or prevailing anchor-based techniques have been proposed to deal with deep learning-based pedestrian detection, it is still a promising yet challenging problem. In this paper, we propose a precise, flexible and thoroughly anchor-free, as well as proposal-free framework named Pedestrian-as-Points Network (PP-Net) for pedestrian detection. Specifically, we model a pedestrian as a single point, i.e., the center point of the instance, and predict the pedestrian scale at each detected center point. In order to achieve higher accuracy, we build a pyramid-like structure based on the backbone as a feature extractor to aggregate multi-level information. In addition, we construct a deep guidance module (DGM) at the top of the backbone, so that the higher-level information can be captured in the process of building a feature pyramid network (FPN) to avoid the dilution of high-level information on the top-down pathway. We further design a feature fusion unit (FFU) to fuse the fine-level features well with the coarse-level semantic information from the top-down pathway. With the only post-processing non-maximum suppression (NMS), we achieve better performance than many state-of-the-arts methods on the challenging pedestrian detection datasets.

【 授权许可】

Unknown