Adaptive Learning Image Tracking Algorithm Based on Characteristic Fusion

Abstract

To solve the problem of model free real-time image tracking, this paper proposes an image tracking algorithm based on characteristic fusion to adaptively learn the historical characteristics of the target(CFAL). In this algorithm, the color feature is redesigned by using the idea of hog to achieve the feature consistency; the particle filter is combined with the reality constraint to improve the matching efficiency. Finally, the algorithm is programmed and tested in reality, animation and video scenes. The experimental results show that adaptive feature learning and weight adjustment can improve the tracking effect and CFAL can track the target stably and reliably under the environment of attitude change and partial occlusion.