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.
Robust infrared small target detection in infrared warning and defending system is a challenging task due to the low signal-to-clutter ratios and complex background. Motivated by human vision system, we proposed a scale adaptive patch-based contrast measure(SPCM) method for infrared small target detection. At the first stage, a patch-based contrast model is established for measuring small target scale response, whose highest response corresponding to the best estimated size of the target, at the same time, filtering out some non-target areas and leave potential candidates. At the Second stage, we calculate local patch contrast at candidate regions with the estimated target scale. Utilizing the just right scale, the patch-based contrast measure could effectively suppress background clutter and extract infrared small target in single image. Finally, an improved adaptive threshold method by using statistical information of candidate target is used to segment infrared small target. In order to verify the effectiveness of the proposed approach, we compared our method with several fixed-scale and multi-scale infrared small detection algorithm. Experimental results indicate that our method is not only able to effectively estimate the actual scale of the target, but also detect weak small target accurately in heterogeneous background with low and comparable false alarm ratio, while achieving three times faster runtime performance than multi-scale algorithm.