Robust Visual Tracking Using Case-Based Reasoning with Confidence

The paper describes a simple but robust framework for visual object tracking in a video sequence. Compared with the existing tracking techniques, our proposed tracking technique has two significant contributions. First, a Case- Based Reasoning (CBR) paradigm is introduced to track the non-rigid object robustly under significant appearance changes without drifting away. Second, it can provide an accurate confidence measurement for each tracked object so that the tracking failures can be identified successfully. Specifically, under this framework, the appearance changes of the object being tracked can be adapted dynamically during tracking via an adaption mechanism of CBR. Hence, an accurate 2D tracking model can be maintained online for each image frame during tracking. Therefore, the proposed tracking technique possesses a self-recovery capability so that the object can be tracked robustly under significant appearance changes without error accumulation. Application was focused on the development of a real-time face tracking system. Via the proposed framework, the built real-time face tracker can track the human face robustly at 26 frames per second under various face orientations, significant facial expression and external illumination changes.