Phased Feature Extraction Network for Vehicle Search Tasks Based on Cross-Camera for Vehicle–Road Collaborative Perception
Author:
Wang Hai1ORCID, Niu Yaqing1, Chen Long2, Li Yicheng2, Luo Tong3ORCID
Affiliation:
1. School of Automotive and Traffic Engineering, Jiangsu University, Zhenjiang 212013, China 2. Automotive Engineering Research Institute, Jiangsu University, Zhenjiang 212013, China 3. School of Automobile and Traffic Engineering, Jiangsu University of Technology, Changzhou 213001, China
Abstract
The objective of vehicle search is to locate and identify vehicles in uncropped, real-world images, which is the combination of two tasks: vehicle detection and re-identification (Re-ID). As an emerging research topic, vehicle search plays a significant role in the perception of cooperative autonomous vehicles and road driving in the distant future and has become a trend in the future development of intelligent driving. However, there is no suitable dataset for this study. The Tsinghua University DAIR-V2X dataset is utilized to create the first cross-camera vehicle search dataset, DAIR-V2XSearch, which combines the cameras at both ends of the vehicle and the road in real-world scenes. The primary purpose of the current search network is to address the pedestrian issue. Due to varying task scenarios, it is necessary to re-establish the network in order to resolve the problem of vast differences in different perspectives caused by vehicle searches. A phased feature extraction network (PFE-Net) is proposed as a solution to the cross-camera vehicle search problem. Initially, the anchor-free YOLOX framework is selected as the backbone network, which not only improves the network’s performance but also eliminates the fuzzy situation in which multiple anchor boxes correspond to a single vehicle ID in the Re-ID branch. Second, for the vehicle Re-ID branch, a camera grouping module is proposed to effectively address issues such as sudden changes in perspective and disparities in shooting under different cameras. Finally, a cross-level feature fusion module is designed to enhance the model’s ability to extract subtle vehicle features and the Re-ID’s precision. Experiments demonstrate that our proposed PFE-Net achieves the highest precision in the DAIR-V2XSearch dataset.
Funder
the National Natural Science Foundation of China Key Research and Development Program of Jiangsu Province
Subject
Electrical and Electronic Engineering,Biochemistry,Instrumentation,Atomic and Molecular Physics, and Optics,Analytical Chemistry
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