关键词: 目标跟踪, 非线性滤波器更新, 非线性HOG特征提取, 对数极坐标尺度适应, 生物视觉感知规律

Abstract: In order to address the problem that the tracking model tends to drift during target tracking and cannot be robustly tracked for targets with diverse morphological changes, a correlation filtering target tracking algorithm based on nonlinear spatio-temporal regularization is proposed according to the law of biological visual perception. Firstly, a temporal regularization term for nonlinear filter update that is close to the power law of human visual perception is proposed in the objective function. Compared to the fixed temporal regularization term in the STRCF (spatio-temporal regularized correlation filter), the temporal regularization term updated by the nonlinear filter can be adaptively updated according to the tracked temporal changes, and the algorithm complexity is reduced by the alternate multiplier method. Then, nonlinear HOG (histogram of oriented gradient) features are extracted and scale adaptation is performed using log-polar coordinates conforming to biological mapping. Finally, occlusion anomaly detection is performed according to the relationship between the maximum response value and average peak-to-correlation energy, which reduces the probability of model drift and enhances the anti-occlusion ability of the algorithm. The experimental results show that the accuracy and success rate of the algorithm tested on the OTB2015 dataset are 89.8% and 83.3%, respectively. Compared with STRCF, the proposed algorithm improves the accuracy rate by 2.5% and the success rate by 3.2%. In the classification comparison of 11 attributes on OTB2013 and OTB2015, the proposed algorithm has higher accuracy and stronger robustness in target tracking under the interference of rotating, low-resolution background, clutter, illumination change and other factors.

Key words: target tracking, nonlinear filter update, nonlinear HOG feature extraction, log-polar coordinate scale adaptation, biological visual perception law