Improving an object tracker for infrared flying bird tracking

We propose an approach to improve the tracking performance of a generic tracker when it is applied to infrared flying bird tracking task. Since the flying bird is a fast-moving small object, its drastic changes of shape and scale, and cluttered background can all cause the performance degradation of generic trackers. Moreover, the gray intensity also weakens the discriminative power of object or background model. In our approach, we apply edge information and segmentation to refine the output of a generic tracker. Edge information as heuristic cues to delimit object region, and a high-order appearance separation term is used to segment finer object region. Their combination enables tracking algorithm to accommodate bird deformation and scale changes. In general, our method takes estimated object location of a generic tracker as input and generates the segmentation output with higher tracking precision and exact object regions. In the framework of online learning, we use segmentation output as the input of online learner to increase the accuracy of learning and enhance the discriminative power of classifier. The experiments are performed on our BIRDSITE-IR dataset. The results demonstrate the effectiveness of our approach and improves the state-of-the-art trackers by 5% at least in average tracking precision.