Learning Patterns of Activity Using Real-Time Tracking
IEEE Transactions on Pattern Analysis and Machine Intelligence
Tracking and Object Classification for Automated Surveillance
ECCV '02 Proceedings of the 7th European Conference on Computer Vision-Part IV
Fast Vehicle Detection with Probabilistic Feature Grouping and its Application to Vehicle Tracking
ICCV '03 Proceedings of the Ninth IEEE International Conference on Computer Vision - Volume 2
Detection and classification of vehicles
IEEE Transactions on Intelligent Transportation Systems
Variable-mass particle filter for road-constrained vehicle tracking
EURASIP Journal on Advances in Signal Processing
Knowledge-Based Road Traffic Monitoring
IWINAC '07 Proceedings of the 2nd international work-conference on Nature Inspired Problem-Solving Methods in Knowledge Engineering: Interplay Between Natural and Artificial Computation, Part II
Occlusion handling based on sub-blobbing in automated video surveillance system
Proceedings of The Fourth International C* Conference on Computer Science and Software Engineering
Vehicle tracking and traffic parameter extraction based on discrete wavelet transform
AI'06 Proceedings of the 19th Australian joint conference on Artificial Intelligence: advances in Artificial Intelligence
A multi-resolution framework for multi-object tracking in Daubechies complex wavelet domain
International Journal of Computational Vision and Robotics
Real time recognition of pedestrian and vehicles from videos
Proceedings of the Second International Conference on Computational Science, Engineering and Information Technology
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Real time road traffic monitoring is one of the challenging problems in machine vision, especially when one is using commercially available PCs as the main processor. In this paper, we describe a real-time method for extracting a few traffic parameters in highways such as, lane change detection, vehicle classification and vehicle counting. In addition, we will explain a real time method for multiple vehicles tracking that has the capability of occlusion detection. Our tracing algorithm uses Kalman filter and background differencing techniques. We used morphological operations for vehicle contour extraction and its recognition. Our algorithm has three phases, detection of pixels on moving objects, detection of a ''Shape of Interest'' in frame sequences and finally determination of relation among objects also in frame sequences. Our system is implemented on a PC with Pentium II 800MHZ CPU. Its processing speed was measured to be 11 frames per second. The accuracy of measurement was 96%.