DocumentCode :
2250463
Title :
Video-based traffic monitoring at day and night vehicle features detection tracking
Author :
Robert, Kostia
Author_Institution :
Smart Transp. & Road-Sensors & Surveillance, NICTA (Nat. ICT Australia), Sydney, NSW, Australia
fYear :
2009
fDate :
4-7 Oct. 2009
Firstpage :
1
Lastpage :
6
Abstract :
Due to the recent progress in computer vision to interpret images and sequence of images, the video camera is a promising sensor for traffic monitoring and traffic surveillance at low cost. This paper focuses on the detection and tracking of multiple vehicles present in the field of view of a camera. Until now, the vehicle detection has been mainly performed by the widely used technique called background subtraction, which is based on detecting changes in an image sequence. While there has been long research on this technique, it still faces many challenges. We present in this paper a new framework to detect vehicles, based on a hierarchy of features detection and fusion. The first layer of the hierarchy extract image features. The next layer fuses image features to detect vehicle features such as headlights or windshields. A last layer fuses the vehicle features to detect a vehicle with more confidence. This approach is thus road illumination agnostic and allows vehicles to be detected day and night. The vehicle features are tracked over frames. We use a constant acceleration tracking model augmented with traffic-domain rules to handle the occlusions challenges.
Keywords :
computer vision; feature extraction; image sensors; image sequences; object detection; road traffic; road vehicles; tracking; video cameras; video surveillance; background subtraction; computer vision; constant acceleration tracking model; image feature extraction; image sequence; multiple vehicle detection; road illumination agnostic; traffic-domain rules; vehicle features detection tracking; video camera; video traffic surveillance; video-based traffic monitoring; Cameras; Computer vision; Computerized monitoring; Costs; Fuses; Image sensors; Image sequences; Surveillance; Vehicle detection; Vehicles; Traffic Monitoring; computer vision; headlights; hierarchical feature-based vehicle detection; machine learning; multiple vehicle detection; multiple vehicle tracking; vehicle appearance; windshields;
fLanguage :
English
Publisher :
ieee
Conference_Titel :
Intelligent Transportation Systems, 2009. ITSC '09. 12th International IEEE Conference on
Conference_Location :
St. Louis, MO
Print_ISBN :
978-1-4244-5519-5
Electronic_ISBN :
978-1-4244-5520-1
Type :
conf
DOI :
10.1109/ITSC.2009.5309837
Filename :
5309837
Link To Document :
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