基于动作模板匹配的弱监督动作定位

为解决视频中的动作定位问题,提出一种基于模板匹配的弱监督动作定位方法。首先在视频的每一帧上给出若干个动作主体位置的候选框,按时间顺序连接这些候选框形成动作提名;然后利用训练集视频的部分帧得到动作模板;最后利用动作提名与动作模板训练模型,找到最优的模型参数。在UCF-sports数据集上进行实验,结果显示,与TLSVM方法相比,所提方法的动作分类准确率提升了0.3个百分点;当重叠度阈值取0.2时,与CRANE方法相比,所提方法的动作定位准确率提升了28.21个百分点。实验结果表明,所提方法不但能够减少数据集标注的工作量,而且动作分类和动作定位的准确率均得到提升。

Weakly supervised action localization based on action template matching

In order to solve the problem of action localization in video, a weakly supervised method based on template matching was proposed. Firstly, several candidate bounding boxes of the action subject position were given on each frame of the video, and then these candidate bounding boxes were connected in chronological order to form action proposals. Secondly, action templates were obtained from some frames of the training set video. Finally, the optimal model parameters were obtained after model training by using action proposals and action templates. In the experiments on UCF-sports dataset, the method has the accuracy of the action classification increased by 0.3 percentage points compared with TLSVM (Transfer Latent Support Vector Machine) method; when the overlapping threshold is 0.2, the method has the accuracy of action localization increased by 28.21 percentage points compared with CRANE method. Experimental results show that the proposed method can not only reduce the workload of dataset annotation, but also improve the accuracy of action classification and action localization.