Estimating Vehicle Velocity Using Image Profiles on Rectified Images
IbPRIA '09 Proceedings of the 4th Iberian Conference on Pattern Recognition and Image Analysis
Vehicle Tracking Using Projective Particle Filter
AVSS '09 Proceedings of the 2009 Sixth IEEE International Conference on Advanced Video and Signal Based Surveillance
IMM-based lane-change prediction in highways with low-cost GPS/INS
IEEE Transactions on Intelligent Transportation Systems
Estimating traffic intensity using profile images on rectified images
ICIP'09 Proceedings of the 16th IEEE international conference on Image processing
A joint watermarking and ROI coding scheme for annotating traffic surveillance videos
EURASIP Journal on Advances in Signal Processing
Integrating the projective transform with particle filtering for visual tracking
Journal on Image and Video Processing - Special issue on advanced video-based surveillance
Behavior pattern extraction by trajectory analysis
Frontiers of Computer Science in China
Advanced formation and delivery of traffic information in intelligent transportation systems
Expert Systems with Applications: An International Journal
Experimental comparison of DWT and DFT for trajectory representation
IDEAL'12 Proceedings of the 13th international conference on Intelligent Data Engineering and Automated Learning
A low-bandwidth camera sensor platform with applications in smart camera networks
ACM Transactions on Sensor Networks (TOSN)
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Intelligent vision-based traffic surveillance systems are assuming an increasingly important role in highway monitoring and road management schemes. This paper describes a low-level object tracking system that produces accurate vehicle motion trajectories that can be further analyzed to detect lane centers and classify lane types. Accompanying techniques for indexing and retrieval of anomalous trajectories are also derived. The predictive trajectory merge-and-split algorithm is used to detect partial or complete occlusions during object motion and incorporates a Kalman filter that is used to perform vehicle tracking. The resulting motion trajectories are modeled using variable low-degree polynomials. A K-means clustering technique on the coefficient space can be used to obtain approximate lane centers. Estimation bias due to vehicle lane changes can be removed using robust estimation techniques based on Random Sample Consensus (RANSAC). Through the use of nonmetric distance functions and a simple directional indicator, highway lanes can be classified into one of the following categories: entry, exit, primary, or secondary. Experimental results are presented to show the real-time application of this approach to multiple views obtained by an uncalibrated pan-tilt-zoom traffic camera monitoring the junction of two busy intersecting highways.