An Improved Kernel Correlation Filter Tracking Combined with Mobilenet SSD

An Improved Kernel Correlation Filter Tracking Combined with Mobilenet SSD

This article mainly solves the problems that exist when using the Kernel Correlation Filter (KCF) for tracking in complex scenarios. To make the algorithm suitable for target tracking under complex conditions such as scale changes, similar interference, and occlusion, a MobileNet SSD (Single Shot Detection) target detection combined with an improved KCF target tracking algorithm is proposed. Firstly, the MobileNet SSD is used to locate the target in the initial frame, and the location is sent to KCF for training. Secondly, aiming at the problem of scale changes, a Binary-Tree scale search strategy is proposed. In this strategy, the scale value is searched in a tree shape according to the response size, which reduces the number of scale searches. Finally, the average peak correlation energy is used for occlusion determination, and the model update strategy is improved, thereby enhancing the algorithm’s ability to track occluded targets. The results of experimental evaluation and comparison on the OTB100 and UAV123 data sets show that when the target has complex conditions such as scale changes, similar interference, occlusion, etc., the proposed algorithm performs well in mainstream related filtering algorithms. Through the quantitative and qualitative analysis of the experimental results, the effectiveness of the proposed algorithm is verified.