基于模式理论的动作识别视频时间结构学习

Xiaoyu Zhang
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引用次数: 0

摘要

针对视频中大量背景信息导致动作判断能力低下的问题,提出了一种基于模式理论的人类复杂动作识别图模型。首先,将视频分成视频单元,每个视频单元对应一个原子动作。视频的原子动作标签通过k-Means初始化。其次,提出关键生成建议模块和解释操作模块,选择重要的前景信息,获得原子动作序列的合理表示;在推理阶段,通过动态时间扭曲算法(DTW)将测试视频的原子动作序列与模板序列进行匹配,得到动作类别。实验结果表明,与大多数现有的人类动作识别模型相比,我们的模型可以解释动作发生的时间过程,并获得更具歧视性的序列表示,可以有效提高动作识别的准确性。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
Learning Temporal Structure of Videos for Action Recognition Using Pattern Theory
Aiming at the problem that a large amount of background information in the videos cause low judgment of actions, this paper proposed a graph model based on pattern theory for human complex action recognition. Firstly, a video is divided into video units and each video unit corresponds to an atomic action. The atomic action labels of videos are initialized by k-Means. Secondly, the key generator proposal module and the interpretative operation module are proposed to select important foreground information and obtain a reasonable representation of atomic action sequences. In the inference stage, the atomic action sequences of test videos are matched with template sequences by the Dynamic Time Warping algorithm (DTW) to obtain the action categories. The experimental results show that compared with the most existing human action recognition models, our model can explain the temporal process of action occurrence and obtain a more discriminatory sequence representation, which can effectively improve the accuracy of action recognition.
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