基于双线程LSTM在线更新的视频追踪算法

A video tracking algorithm based on dual-thread LSTM online update

针对视频追踪中基于孪生网络的追踪算法在对有遮挡物或运动突变的物体进行追踪定位时会出现定位不准确的问题,设计了在线更新网络的视频追踪算法TripLT。该算法采用循环神经网络进行目标位置的预测,并采用全卷积神经网络对目标进行相似度的判定。TripLT算法可预测下一帧的目标位置,以摆脱遮挡物的影响,并且TripLT算法采用在线更新的机制,避免了运动突变的干扰。在数据集VOT和OTB100上的实验结果表明,和已有算法相比,TripLT算法表现出更好的性能。

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.