Vehicle Detection Based on Hierarchical Structure and Grammar Model for Complex Traffic Surveillance
Abstract：The traffic video surveillance system is an important part of intelligent transportation systems (ITS) and vehicle detection is a key technology in video-based traffic surveillance system. However, the complex traffic environments have brought about huge challenges to vehicle detection, including vehicle occlusion and stationary vehicle under congestion scenarios, lighting variation under bad weathers and at different times of day. By analyzing the problems of existing traffic surveillance system and exploring the new demands for traffic surveillance, this project studies vehicle detection technology from the point of view of object hierarchical structure and aims to realize the vehicle detection technology for complex traffic surveillance. The main research contents include: 1)The vehicle object is divided into a hierarchical structure, by analyzing the characteristics of traffic scenes and the vehicle itself; 2) According to the hierarchical structure, object detection grammars are designed for describing the composition of vehicle parts, measuring their structural relationship and finally realizing the fusion of vehicle parts. The vehicle detection method, which is realized in this project, can effectively adapt to stopping vehicle, partial occlusion, multiple vehicle views, multiple vehicle types and more practical problems. The detection model has universal applicability. The achievements of this project can be applied in the traffic video surveillance systems, by supporting and supplementing algorithms for intelligent transportation applications such as traffic information collection and traffic incident detection.
Keywords: vehicle detection; hierarchical structure; object detection grammars; traffic surveillance
Contact:
TIAN Bin
E-mail: bin.tian@ia.ac.cn
The State Key Laboratory of Management and Control for Complex Systems