Abstract: Aiming at the problem of inaccurate positioning of the tracking algorithm based on the twin network when tracking and locating objects with obstructions or sudden changes in motion, an online update network video tracking algorithm, TripLT, is designed, a recurrent neural network is used to predict the target position, and a full convolutional neural network is used to determine the similarity of the target. The TripLT algorithm can predict the target position of the next frame to get rid of the influence of occluders, and it uses an online update mechanism to avoid the interference effects of sudden changes in motion. Experiments on the data set VOT and OTB100 show that the TripLT algorithm shows better performance than other algorithms.

Key words: video tracking, twin network, recurrent neural network, full convolutional neural network, online update