三联神经网络与区域自适应策略融合的目标跟踪方法

In order to solve the problems of fast motion blur, background similar interference and target state change in the process of target tracking, a target tracking method (TAA+TripleRPN) that combines the triple area candidate neural network (tripleRPN) algorithm with the tracking area adaptive strategy (TAA) was proposed based on siamese network tracking algorithm. The triple-area candidate neural network updates the network matching template in real time based on the current tracking results, which improves the sensitivity of the tracker to changes in the target state. Through the regional adaptive strategy, based on the scores of the classification candidates of the regional candidate regression network, the two groups of network outputs are selected optimally, which improves the robustness of the algorithm's long-term tracking. For the problems of similar background interferences and target state changes, the TAA+TripleRPN tracker can achieve better tracking performance. On the OTB2015 dataset, the algorithm has an AUC of 66.31% and a CLE of 88.28%. The verification and application are implemented in actual scenarios, and the tracking effect is good.