Pfinder: Real-Time Tracking of the Human Body
IEEE Transactions on Pattern Analysis and Machine Intelligence
Multiple video object tracking in complex scenes
Proceedings of the tenth ACM international conference on Multimedia
Non-parametric Model for Background Subtraction
ECCV '00 Proceedings of the 6th European Conference on Computer Vision-Part II
Evaluating Hardware Design Principles for the Development of Computer Based Systems
ECBS '96 Proceedings of the IEEE Symposium and Workshop on Engineering of Computer Based Systems
A Vision-Based Vehicle Identification System
ICPR '04 Proceedings of the Pattern Recognition, 17th International Conference on (ICPR'04) Volume 4 - Volume 04
Pattern Analysis & Applications
Context and Hierarchy in a Probabilistic Image Model
CVPR '06 Proceedings of the 2006 IEEE Computer Society Conference on Computer Vision and Pattern Recognition - Volume 2
Improved Vehicle Classification in Long Traffic Video by Cooperating Tracker and Classifier Modules
AVSS '06 Proceedings of the IEEE International Conference on Video and Signal Based Surveillance
Is abstraction the key to computing?
Communications of the ACM
Robust vehicle and traffic information extraction for highway surveillance
EURASIP Journal on Applied Signal Processing
Robust background subtraction with foreground validation for urban traffic video
EURASIP Journal on Applied Signal Processing
Application of transferable belief model to navigation system
Integrated Computer-Aided Engineering - Informatics in Control, Automation and Robotics
Trajectory planning and sliding-mode control based trajectory-tracking for cybercars
Integrated Computer-Aided Engineering - Informatics in Control, Automation and Robotics
A knowledge retrieval model using ontology mining and user profiling
Integrated Computer-Aided Engineering
Vehicular traffic density estimation via statistical methods with automated state learning
AVSS '07 Proceedings of the 2007 IEEE Conference on Advanced Video and Signal Based Surveillance
A multi-agent system for managing adverse weather situations on the road network
Integrated Computer-Aided Engineering
An adaptive, real-time, traffic monitoring system
Machine Vision and Applications
Vehicle Counting and Trajectory Detection Based on Particle Filtering
SIBGRAPI '10 Proceedings of the 2010 23rd SIBGRAPI Conference on Graphics, Patterns and Images
Classification of traffic video based on a spatiotemporal orientation analysis
WACV '11 Proceedings of the 2011 IEEE Workshop on Applications of Computer Vision (WACV)
Integrated Computer-Aided Engineering
Vehicle classification from traffic surveillance videos at a finer granularity
MMM'07 Proceedings of the 13th international conference on Multimedia Modeling - Volume Part I
Evaluation of background subtraction techniques for video surveillance
CVPR '11 Proceedings of the 2011 IEEE Conference on Computer Vision and Pattern Recognition
IEEE Transactions on Intelligent Transportation Systems
Image analysis and rule-based reasoning for a traffic monitoring system
IEEE Transactions on Intelligent Transportation Systems
Automatic traffic surveillance system for vehicle tracking and classification
IEEE Transactions on Intelligent Transportation Systems
A survey on visual surveillance of object motion and behaviors
IEEE Transactions on Systems, Man, and Cybernetics, Part C: Applications and Reviews
A Review of Computer Vision Techniques for the Analysis of Urban Traffic
IEEE Transactions on Intelligent Transportation Systems
Lane mark segmentation and identification using statistical criteria on compressed video
Integrated Computer-Aided Engineering
Pedestrian detection in far infrared images
Integrated Computer-Aided Engineering
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Interest in the application of computer vision techniques to automatic video-based analysis of traffic is high at present. This is due in part to the capabilities of video sensors, as well as to social demands for traffic safety. In general, these systems are cheaper and less disruptive than other kinds of devices like loop detectors for traffic monitoring. Automatic traffic surveillance is, however, still a challenging problem when we consider many of the practical difficulties involved i.e. limited number of cameras and positions of these with respect to the scene, variable illumination and weather conditions, intrinsic complexity of analyzed traffic events, need for a real-time frame rate processing, among others. In this paper, we propose a multi-level framework for automatic analysis of complex traffic videos which present different kind of variations. The accurate and efficient extraction of relevant scene information from the video frames is performed in a hierarchical bottom-up form using the system presented. First of all, foreground moving pixels are detected in each frame using a proposed method of adaptive background subtraction. After that, these pixels are grouped into blobs if they share some common properties. Blobs detected in predefined scene entry regions are identified as vehicles and these are tracked along the controlled road area. At the upper level, some traffic monitoring statistics and also related linguistic reports on the evolution of traffic in the scene are generated periodically. Experimental results on the adaptive background method proposed, as well as regarding its integration in the multi-level traffic analysis system, are very satisfactory for the traffic videos analyzed.