A Novel Long Short-Term Memory Learning Strategy for Object Tracking

Abstract

In this paper, a novel integrated long short-term memory (LSTM) network and dynamic update model are proposed for long-term object tracking in video images. The LSTM network tracking method is introduced to improve the effect of tracking failure caused by target occlusion. Stable tracking of the target is achieved using the LSTM method to predict the motion trajectory of the target when it is occluded and dynamically updating the tracking template. First, in target tracking, global average peak-to-correlation energy (GAPCE) is used to determine whether the tracking target is blocked or temporarily disappearing such that the follow-up response tracking strategy can be adjusted accordingly. Second, the data with target motion characteristics are utilized to train the designed LSTM model to obtain an offline model, which effectively predicts the motion trajectory during the period when the target is occluded or has disappeared. Therefore, it can be captured again when the target reappears. Finally, in the dynamic template adjustment stage, the historical information of the target movement is combined, and the corresponding value of the current target is compared with the historical response value to realize the dynamic adjustment of the target tracking template. Compared with the current mainstream efficient convolution operators, namely, the E.T.Track, ToMP, KeepTrack, and RTS algorithms, on the OTB100 and LaSOT datasets, the proposed algorithm increases the distance precision by 9.9% when the distance threshold is 5 pixels, increases the overlap success rate by 0.94% when the overlap threshold is 0.75, and decreases the center location error by 18.9%. The proposed method has higher tracking accuracy and robustness and is more suitable for long-term tracking of targets in actual scenarios than are the main approaches.