Tracking an Object over 200 FPS with the Fusion of Prior Probability and Kalman Filter

Efficient object tracking is a challenge problem as it needs to distinguish the object by learned appearance model as quickly as possible. In this paper, a novel robust approach fusing the prediction information of Kalman filter and prior probability is proposed for tracking arbitrary objects. Firstly, we obtain an image patch based on predicted information by fusing the prior probability and Kalman filter. Secondly, the samples derived from the obtained image patch for our tracker are entered into support vector machine (SVM) to classify the object, where these samples need to be extracted features by Histogram of Oriented Gradients (HOG). Our approach has two advantages: efficient computation, and certain anti-interference ability. The samples obtained from image patch is less than that obtained from image, which makes SVM model more efficient in classification and reduces interference outside the image patch. Experimentally, we evaluate our approach on a standard tracking benchmark that includes 50 video sequences to demonstrate our tracker's nearly state-of-theart performance compared with 5 trackers. Furthermore, because extracting samples and classifying HOG features is computationally very cheap, our tracker is much faster than these mentioned trackers. It achieves over 200 fps on the Intel i3 CPU for tracking an arbitrary object on benchmark.