Tracking and counting vehicles in traffic video sequences using particle filtering

This paper presents a new method to track and count vehicles in video traffic sequences. The proposed method uses image processing, particle filtering, and motion coherence to group particles in videos, forming convex shapes that are analyzed for potential vehicles. This analysis takes into consideration the convex shape of the objects and background information to merge or split the groupings. After a vehicle is identified, it is tracked using the similarity of color histograms on windows centered at the particle locations.

[1]  ZuWhan Kim.  Real time object tracking based on dynamic feature grouping with background subtraction , 2008, 2008 IEEE Conference on Computer Vision and Pattern Recognition.

[2]  Lei Xie,et al.  Real-time vehicles tracking based on Kalman filter in a video-based ITS , 2005, Proceedings. 2005 International Conference on Communications, Circuits and Systems, 2005..

[3]  P. Mahalanobis.  On the generalized distance in statistics , 1936 .

[4]  Carlo Tomasi,et al.  Good features to track , 1994, 1994 Proceedings of IEEE Conference on Computer Vision and Pattern Recognition.

[5]  Dorin Comaniciu,et al.  Real-time tracking of non-rigid objects using mean shift , 2000, Proceedings IEEE Conference on Computer Vision and Pattern Recognition. CVPR 2000 (Cat. No.PR00662).

[6]  Rafael C. González,et al.  Local Determination of a Moving Contrast Edge , 1985, IEEE Transactions on Pattern Analysis and Machine Intelligence.

[7]  Jacob Scharcanski,et al.  A Particle-Filtering Approach for Vehicular Tracking Adaptive to Occlusions , 2011, IEEE Transactions on Vehicular Technology.

[8]  Zheng Li-xin,et al.  Block Matching Algorithms for Motion Estimation , 2005 .

[9]  Christopher G. Harris,et al.  A Combined Corner and Edge Detector , 1988, Alvey Vision Conference.

[10]  Azeddine Beghdadi,et al.  Vehicle Tracking by non-Drifting Mean-shift using Projective Kalman Filter , 2008, 2008 11th International IEEE Conference on Intelligent Transportation Systems.

[11]  Aura Conci,et al.  Video-Based Distance Traffic Analysis: Application to Vehicle Tracking and Counting , 2011, Computing in Science & Engineering.

[12]  Kunfeng Wang,et al.  Video processing techniques for traffic flow monitoring: A survey , 2011, 2011 14th International IEEE Conference on Intelligent Transportation Systems (ITSC).

[13]  D.M. Mount,et al.  An Efficient k-Means Clustering Algorithm: Analysis and Implementation , 2002, IEEE Trans. Pattern Anal. Mach. Intell..