北京交通大学学报2017，Vol.41Issue(5)：66-72,7.DOI:10.11860/j.issn.1673-0291.2017.05.010
基于图论的复杂交通环境下车辆检测方法
Graph-based vehicle detection in complex traffic environment
摘要
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.关键词
信息处理/车辆检测/车辆候选区域/多视角分类器Key words
information processing/vehicle detection/vehicle candidate location/multi-view classifiers分类
信息技术与安全科学引用本文复制引用
苏帅,袁雪,张立平,李寒松..基于图论的复杂交通环境下车辆检测方法[J].北京交通大学学报,2017,41(5):66-72,7.基金项目
国家自然科学基金项目(61301186,61673047) （61301186,61673047）
北京市科委重大研究专项(SX2016-04)National Natural Science Foundation of China(61301186,61673047) （SX2016-04）
Beijing Municipal Science & Technology Commission Major Program(SX2016-04) （SX2016-04）
