Object Tracking Algorithm Based on Correlation Filtering and Convolution Residuals Learning

Aiming at the problem of insufficient expression ability of traditional single manual feature and model degradation caused by error accumulation in the process of model updating in complex scenes, Based on this, the object tracking algorithm based on correlation filtering and convolution residual learning is proposed. The multifeature correlation filtering algorithm is defined as a layer in the neural network, and the feature extraction, response graph generation, and model update arc integrated into the end-to-end neural network for model training. In order to reduce the degradation of model during online updating, the residual learning mode is introduced to guide model updating. The proposed method is validated on the benchmark datasets OTB-2013 and OTI-3-2015. The experimental results show that the proposed algorithm can effectively deal with motion blur, deformation, and illumination in the complex scene, and has high tracking accuracy and robustness.