A method of adaptive learning rate tracking for embedded device based correlation surface evaluation

The Accuracy of correlation filtering trackers have got great improvement because of using high dimension features, but its real-time performance became worsen. And we often have the meet of running tracker on embedding device, in this case, we need less calculation. It is all known that the model updating strategy is also important for tracking performance. The fixed learning rate model updating strategy is difficult to deal with the situation that the object changes rapidly or slowly. For the problem, a new correlation surface quality evaluation metric is proposed in this paper. Meanwhile, we consider the occlusion of the object, and propose the occlusion judgment algorithm. Finally, the learning rate of model is updated adaptively according to the change speed of the object and whether the object is occluded. We further conduct experiment on the OTB50 dataset. Experimental results show that the correlation tracker with gray feature can improve the tracking accuracy by about 3% compared with MOSSE tracker, after adopting the learning rate adaptive strategy proposed in this paper and maintain high speed on embedding device.