Maneuvering Target Tracking in Cluttered Background Based on Color Invariance and Support Vector Machine

Maneuvering targets tracking in cluttered environment is a challenging problem in computer vision because of the difficulty of distinguishing the target from the background. In this paper, we treat tracking as a binary classification problem and employ support vector machine to suppress the background. In order to enhance the robustness against illumination changes, we propose to combine color invariance with traditional RGB values to train the SVM. First, we use expectation maximization algorithm to extract the target from the environment; then, RGB and color invariance values are used to train SVM. In the incoming frames, pixels in regions of interest are classified by SVM and the confidence map is produced, which will afterward be used by traditional tracking approach to track the target, in this paper, we employ particle filter. Experimental results on challenging sequences validate the effectiveness of the proposed method in cluttered background target tracking.