%0 Journal Article %A HAN Ming %A WANG Jingqin %A WANG Jingtao %A MENG Junying %T Research on Object Tracking Algorithm Based on Cascading Feature Fusion of Siamese Network %D 2022 %R 10.3778/j.issn.1002-8331.2108-0416 %J Computer Engineering and Applications %P 208-218 %V 58 %N 6 %X It is difficult to accurately extract rich feature information in the process of target tracking under complex environments such as illumination variation, occlusion, background clutters and deformation, which is easy to lead to the object shift or tracking loss. Because the low-level features have high resolution of multilayer neural network, which is suitable for positioning the object. While the high-level features have rich semantic information and are suitable for object classification. To take full use of the advantage of the multilayer neural network, the siamese network algorithm of cascading feature fusion for object tracking is proposed. The ResNet-50 network is improved, which is reduced the model parameters and computation, and the tracking speed is improved. The cascade feature fusion strategy is adopted to cascade the three layers of features in the last stage of ResNet-50, and to effectively extract the high-level semantic information and low-level spatial information of the object, so as to achieve the accurate multi-feature representation of the object. In the process of object tracking, only the first frame is used as the object template most of the algorithm, which leads to the object template degradation. The template update mechanism is introduced, and the similarity threshold method is used to update the template in real time. The extensive comparative experiments are conducted on the OBT2015, VOT2016 and VOT2018. The experimental results show that the proposed algorithm has higher tracking accuracy and stronger robustness in complex scenes, and has a stronger competitive advantage compared with other algorithms. %U http://cea.ceaj.org/EN/10.3778/j.issn.1002-8331.2108-0416