Infrared target tracking based on selective convolution features

Infrared target tracking is heavily influenced by illumination variation, small size and complex background, and the lack of target information makes the algorithm lose targets easily. Therefore, an algorithm based on convolution features and feature selection method is presented in this paper to track IR targets. First, several filters in target patches of the first frame are used to obtain strong features. Then, the boosting method is utilized to train the features with redundant information, thus, the algorithm performance of accuracy and execution efficiency can be improved. Finally, particle weights are represented by the response of the native Bayes classifier. Experimental results show that the presented algorithm obtains good performance.