基于多层卷积特征的自适应决策融合目标跟踪算法

针对目标快速运动、遮挡等复杂视频场景中目标跟踪鲁棒性差和跟踪精度低的问题，该文提出一种基于多层卷积特征的自适应决策融合目标跟踪算法(ASFTT)。首先提取卷积神经网络(CNN)中帧图像的多层卷积特征，避免网络单层特征表征目标信息不全面的缺陷，增强算法的泛化能力；使用多层特征计算帧图像相关性响应，提高算法的跟踪精度；最后该文使用自适应决策融合算法将所有响应中目标位置决策动态融合以定位目标，融合算法综合考虑生成响应的各跟踪器的历史决策信息和当前决策信息，以保证算法的鲁棒性。采用标准数据集OTB2013对该文算法和6种当前主流跟踪算法进行了仿真对比，结果表明该文算法具有更加优秀的跟踪性能。

Adaptive Strategy Fusion Target Tracking Based on Multi-layer Convolutional Features

To solve the problems of low robustness and tracking accuracy in target tracking when interference factors occur such as target fast motion and occlusion in complex video scenes, an Adaptive Strategy Fusion Target Tracking algorithm (ASFTT) is proposed based on multi-layer convolutional features. Firstly, the multi-layer convolutional features of frame images in Convolutional Neural Network(CNN) are extracted, which avoids the defect that the target information of the network is not comprehensive enough, so as to increase the generalization ability of the algorithm. Secondly, in order to improve the tracking accuracy of the algorithm, the multi-layer features are performed to calculate the correlation responses, which improves the tracking accuracy. Finally, the target position strategy in all responses are dynamically merged to locate the target through the adaptive strategy fusion algorithm in this paper. It comprehensively considers the historical strategy information and current strategy information of each responsive tracker to ensure the robustness. Experiments performed on the OTB2013 evaluation benchmark show that that the performance of the proposed algorithm are better than those of the other six state-of-the-art methods.