Scene Adaptive Object Tracking Combining Local Feature and Color Feature

Abstract

Scene changes like scale, rotation, illumination and occlusion often occur in video sequences, which raise challenges to robust object tracking. This paper presents a new on-line object tracking method adapting to different scene changes, by combining local feature and color feature. First, object tracking is treated as a keypoint matching problem. SURF features are detected, described and further categorized according to different scene changes and undergo dynamic clustering. In addition, color feature is constructed to better choose the image domain for matching. Online updating is performed on SURF feature and color feature once tracking is successful. Experimental results validate the robustness and accuracy of the proposed method under complex scene changes.