关键词: 核相关滤波, 目标模板, 多通道特征, 择优并行更新, 卷积神经网络

Abstract: Aiming at the limitations of single features and insufficiencies update of target templates and feature appearance templates intraditional kernel tracker, a multi-channel feature and preferred parallel update for kernel correlation filter tracking is proposed. The multi-channel feature extraction method is adopted which means the upper branch uses the convolutional neural network to extract the depth features, and the lower branch combines the features of HOG and CN for training and tracking. A novel preferred parallel update method is proposed for updating the target templates and feature appearance templates. In the current frame, the maximum response value in both branches is considered as the optimal target position. In the next frame, the templates of the two branches are updated simultaneously with the parameters of the optimal position of the previous frame until the end of the tracking. The optimal parallel update of multiple branches makes up for the deficiency of single branch update. Experiments show that this algorithm can achieve more robust tracking result under different challenge factors.

Key words: kernel correlation filtering, target template, multi-channel feature, preferred parallel update, convolutional neural network