Abstract: A siamese network with temporal-spatial attention mechanism is proposed to tackle the problem of poor accuracy in dealing with fast motion and background clutter in the current algorithms. A temporal attention module is designed， the features of multi-frames are fused according to the contribution of each reference frame，and the weights are assigned adaptively to construct an effective temporal fused template. The spatial attention module is adopted to percept the whole tracking image by non-local operation，which can improve the discriminative ability of the network. During the training stage， the Focal Loss is utilized to train the network model，which can balance the proportion of positive and negative samples，and improve the ability of model to distinguish the hard samples. Several simanation experiments were conducted on the OTB2015 and VOT2016 benchmark to evaluate the performance of the proposed algorithm，and compared it with the state-of-the-art algorithms, i.e., ECO, DSST, HDT, CFNet, KCF, SRDCF, SiamFC, DCFnet, MEEM, SiamVGG, BACF and ANT algorithms. The experimental results demonstrate that the proposed siamese network tracking model with temporal-spatial attention property can well handle the fast motion and background clutter situation，and effectively improve the performance of baseline algorithm.

Key words: objecttracking, siamesenetwork, temporalattention, spatialattention