Target tracking algorithm based on image matching and improved kernel correlation filter

In the classical kernel correlated target tracking algorithms, most of them initialize the target detector by importing the manually marked targets. Moreover, the anti-occlusion ability of the correlated filtering algorithm is poor, which leads to the model drift in complex scenes. This work combined the image matching technology with the target tracking technology. In the case of a certain similarity between the input image and the target, the improved SIFT algorithm SIFT-BRISK is used to complete the initialization of the target detector, which can realize the target tracking in more common cases. And then APCE confidence level is introduced to judge whether the target tracking performance is well, adapt the update strategy of keeping track in the high confidence, dynamically adjusting the tracking model in the low confidence, and improve the details of the kernel correlated target tracking algorithm. Thus, the drift of the training model is suppressed to a certain extent. Finally, the algorithm is tested under the OTB-2015 dataset and the result proves that the improved algorithm has better accuracy and higher speed than traditional target tracking algorithms.