Adaptive context-aware correlation filter tracking

For the context-aware correlation filtering tracking algorithm, when extracting the background information around the target to train the filter, the time consistency of the filter is not considered. When the appearance of the target changes suddenly, the filter cannot adapt to the change of the target and background information in two consecutive frames, and the target drift is easy to occur. This paper proposes an adaptive context-aware correlation filtering tracking algorithm. First, the background information around the target is learned into the filter to enhance the filter s ability to classify the background information and the target is added, and the time perception term is added to ensure that the filter for learning two consecutive images is as consistent as possible. Then, linear interpolation method is used to determine the target position. In the model update stage, occlusion discrimination is introduced to determine whether the target is occluded or not based on the average peak correlation energy. Finally, a large number of comparative experiments are carried out with the current mainstream algorithm on the data set OTB100. Experimental results show that the precision and success rate of the proposed algorithm on the data set OTB100 are 0.798 and 0.722, respectively. Compared with other mainstream algorithms, the proposed algorithm also has better tracking effect under complex conditions such as fast motion, occlusion, and illumination change.