ing at the problem of tracking failure due to fast motion and scale variation during object tracking, an object scale adaptation tracking based on full-convolutional siamese networks is proposed. First, a full-convolutional symmetric network is constructed using MatConvNet framework, and the multidimensional feature maps of template images and experimental images are obtained by using the trained networks. Through the cross-correlation operation, the point with the highest confidence score is selected as the center of the tracked target. Then, through multi-scale sampling at the center, the error samples that are less than half the template variance are filtered out. The probability histograms of target templates and samples are built. The Hellinger distance between the template and the samples is calculated, and the appropriate scale is selected as the scale of the target tracking window. Experiments on the OTB-13 dataset are carried out. Compared with other tracking algorithms, the tracking success rate of proposed method is 0.832, and the precision is 0.899, which are higher than that of other algorithms, and the average tracking speed is achieved 42.3 frame/s, meeting the needs of real-time object tracking. Selecting the tracking sequences with fast motion or scale change attributes for further testing, the tracking performance of proposed method is still higher than other algorithms.