This paper describes a feature-based tracking system that can track moving objects with an active camera. A robust camera motion estimation algorithm is proposed to obtain a stable global motion for feature tracking. After we identify background and foreground motions based on dominant motion estimates, we estimate camera motion on the background by applying a parametric affine motion model. After compensating for camera motion, we trace multiple corner features in the scene and segment foreground objects by clustering motion trajectories of the corner features. We can also command the pan-tilt controller to position the moving object at the center of the camera.
This paper describes an algorithm for multiple object tracking that takes a new occlusion reasoning approach. In order to track individual objects under occlusion conditions, we design a 2D token-based tracking system using Kalman filtering. The proposed tracking system consists of two parts: object detection and tracking, and occlusion reasoning using feature matching. The object detection and tracking part finds moving objects from their background. For object detection, we develop an adaptive background update technique. By tracking individual objects with segmentation information, we generate motion trajectories. Computer simulation of the proposed scheme demonstrates its robustness to various occlusion conditions for several test sequences.
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