Intelligent feature-guided multi-object tracking using Kalman filter

Kalman filtering, a recursive state estimation filter is a robust method for tracking objects. It has been proven that Kalman filter gives a good estimation when tested on various tracking systems. However, unsatisfying tracking results may be produced due to different real-time conditions. These conditions include: inter-object occulusion and separation which are observed when objects are being tracked in real-time. Thus, it is challenging to handle for the classical Kalman filter. In this paper, we proposed an idea of intelligent feature-guided tracking using Kalman filtering. A new method is developed named Correlation-Weighted Histogram Intersection (CWHI), in which correlation weights are applied to Histogram Intersection (HI) method. We focus on multi-object tracking in traffic sequences and our aim is to achieve efficient tracking of multiple moving objects under the confusing situations. The proposed algorithm achieves robust tracking with 97.3% accuracy and 0.07% covariance error in different real-time scenarios.

[1]  Andrzej Czyzewski,et al.  Examining Kalman Filters Applied to Tracking Objects in Motion , 2008, 2008 Ninth International Workshop on Image Analysis for Multimedia Interactive Services.

[2]  R. E. Kalman,et al.  A New Approach to Linear Filtering and Prediction Problems , 2002 .

[3]  Shaogang Gong,et al.  Tracking multiple people with a multi-camera system , 2001, Proceedings 2001 IEEE Workshop on Multi-Object Tracking.

[4]  Hung Hai Bui,et al.  Mutliple camera coordination in a surveillance system , 2003 .

[5]  Patrick Pérez,et al.  Color-Based Probabilistic Tracking , 2002, ECCV.

[6]  Cedric Nishan Canagarajah,et al.  Video object tracking using region split and merge and a Kalman filter tracking algorithm , 2001, Proceedings 2001 International Conference on Image Processing (Cat. No.01CH37205).

[7]  Yanxi Liu,et al.  Online Selection of Discriminative Tracking Features , 2005, IEEE Trans. Pattern Anal. Mach. Intell..

[8]  Greg Welch,et al.  An Introduction to Kalman Filter , 1995, SIGGRAPH 2001.

[9]  Michael J. Swain,et al.  Color indexing , 1991, International Journal of Computer Vision.

[10]  Feng Liu,et al.  Robust color-based tracking , 2004, Third International Conference on Image and Graphics (ICIG'04).

[11]  Quan Pan,et al.  Real-time multiple objects tracking with occlusion handling in dynamic scenes , 2005, 2005 IEEE Computer Society Conference on Computer Vision and Pattern Recognition (CVPR'05).

[12]  Xia Limin.  Object tracking using color-based Kalman particle filters , 2004, Proceedings 7th International Conference on Signal Processing, 2004. Proceedings. ICSP '04. 2004..

[13]  Yaonan Wang,et al.  Multi-moving targets detecting and tracking in a surveillance system , 2004, Fifth World Congress on Intelligent Control and Automation (IEEE Cat. No.04EX788).