Graph-based vehicle detection in complex traffic environment
SU Shuai
YUAN Xue
ZHANG Liping
LI Hansong
Abstract：The majority of the existing graph-based vehicle-detection systems make use of sliding-window paradigm for vehicle-candidate regions location.In order to improve the speed of vehicle detection and reduce the computational complexity,a new vehicle detection method based on graph theory is proposed in this paper.The algorithm uses Simple Linear Iterative Clustering (SLIC) algorithm to obtain images with several super-pixel nodes for each image,and analyzes the relationship among the nodes to determine the vehicle candidate region finally.In the detection stage,multi-view detectors are established by training the vehicle images which seen as the positive samples and collected on each distinct view.Based on the geometrical information of the bounding boxes,the suitable viewpoint detectors are selected from the multi-view detectors.The results of the public traffic analysis dataset (KITTI) show that the proposed approach leads to better performances when compared with the current stateof-the-art methods with the same feature extraction and classifier algorithms.Moreover,it can also yield better results under the complex background.
Keywords：information processingvehicle detectionvehicle candidate locationmulti-view classifiers
Publication Date：2017-01-01
Online Publishing Date：2025-08-15(First online date of this platform, not the publication date of the document)
Pages：7( 66-72 )