Open AccessOn‐line vehicle detection at nighttime‐based tail‐light pairing with saliency detection in the multi‐lane intersection
Author(s) -
Gao Fei,
Ge Yisu,
Lu Shufang,
Zhang Yuanming
Publication year - 2019
Publication title -
iet intelligent transport systems
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 0.579
H-Index - 45
eISSN - 1751-9578
pISSN - 1751-956X
DOI - 10.1049/iet-its.2018.5197
Subject(s) - intersection (aeronautics) , computer vision , computer science , artificial intelligence , pairing , saliency map , line (geometry) , frame (networking) , object detection , advanced driver assistance systems , real time computing , pattern recognition (psychology) , image (mathematics) , engineering , mathematics , physics , telecommunications , superconductivity , geometry , quantum mechanics , aerospace engineering
A nighttime vehicle detection method in the multi‐lane intersection has been proposed based on saliency detection for traffic surveillance system in this study. Frame difference method is applied to detect moving objects at first, and all the rear lights of vehicles are extracted based on saliency map and colour information. Second, vehicles are detected through pairing off all the lamps, which include such steps as rechecking tail‐lamp pairs by using prior knowledge, eliminating repaired tail‐lamps on the same vehicle and removing the paired lamps across two lanes. Furthermore, to detect the vehicles that only have a single valid tail‐lamp, a proving approach for virtual tail‐lamp is investigated. Finally, the proposed method is verified to be more reliable and faster for nighttime vehicle detection by comparing with other detection methods, which can satisfy real‐time requirements of a vehicle detection system with good performance.