Feature-Based Object Tracking with an Active Camera

This paper describes a new feature-based tracking system that can track moving objects with a pan-tilt camera. After eliminating the global motion of the camera movement, the proposed tracking system traces multiple corner features in the scene and segments foreground objects by clustering the motion trajectories of the corner features. We propose an efficient algorithm for clustering the motion trajectories. Key attributes for classifying the global and local motions are positions, average moving directions, and average moving magnitude of each corner feature. We command the pan-tilt controller to position the moving object at the center of the camera. The proposed tracking system has demonstrated good performance for several test video sequences.

[1]  Yo-Sung Ho,et al.  ROBUST VEHICLE MOTION DETECTION AND TRACKING FOR TRAFFIC SURVEILLANCE , 1999 .

[2]  Mubarak Shah,et al.  The trajectory primal sketch: a multi-scale scheme for representing motion characteristics , 1989, Proceedings CVPR '89: IEEE Computer Society Conference on Computer Vision and Pattern Recognition.

[3]  J. O'Rourke,et al.  Model-based image analysis of human motion using constraint propagation , 1980, IEEE Transactions on Pattern Analysis and Machine Intelligence.

[4]  Jitendra Malik,et al.  A real-time computer vision system for measuring traffic parameters , 1997, Proceedings of IEEE Computer Society Conference on Computer Vision and Pattern Recognition.

[5]  Hugh F. Durrant-Whyte,et al.  A Fully Decentralized Multi-Sensor System For Tracking and Surveillance , 1993, Int. J. Robotics Res..

[6]  Kyu Tae Park,et al.  An Algorithm of Moving Object Extraction Under Visual Tracking without Camera Calibration , 1995 .

[7]  G. Johansson Visual perception of biological motion and a model for its analysis , 1973 .