A method of real-temporal object tracking combined the temporal information and spatial information

Abstract

The purpose of single target tracking is to accurately and continuously locate a specific object when it is moving. However, when the objects encounter with fast movement, severe occlusion, too small size, and the same local features, the tracking algorithm which based on correlation filter or convolutional neural network will appear the positioning error phenomenon. Aiming at the above problems, this paper designs a single target tracking algorithm: relative temporal spatial network (RTSnet). RTSnet is a multi-thread network that composed of Relative temporal Information Network (RTInet) and Relative Spatial Information Network (RSInet). RTInet is designed on the basis of LSTM, and it has the predictable characteristics of temporal. It mainly obtains the relative temporal information between the frames before and after the target. RSInet, an improved twin network based on the Triplet Network, has the effect of similarity determination which can to obtain the spatial information between the frames before and after the target. In the experiments, the RTSnet is trained by using LASOT data set and verified by using the LASOT test set and the OTB100 data set. In the test set of LASOT, the accuracy of RTSnet reaches 85.5%, Trans-T reaches 62.3% and STMTrack reaches 57.4%. Meanwhile, its tracking speed reaches 117.3fps due to the RTSnet adopts dual-thread operation. On the OTB100 data-set, the accuracy of RTSnet is 81.1%.