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首页> 外文会议>2017 International Conference on Security, Pattern Analysis, and Cybernetics >Spatial-temporal histograms of gradients and HOD-VLAD encoding for human action recognition
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Spatial-temporal histograms of gradients and HOD-VLAD encoding for human action recognition

机译：梯度时空直方图和人类行为识别的HOD-VLAD编码

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Automatic human action recognition is a core functionality of systems for video surveillance and human object interaction. In the whole recognition system, feature description and encoding represent two crucial key steps. In order to construct a powerful action recognition framework it is important that the two steps must provide reliable performance. In this paper, we proposed a new human action feature descriptor which is called spatial-temporal histograms of gradients (SPHOG). SPHOG is based on the spatial and temporal derivation signal, which extracts the gradient changes between consecutive frames. Compare to the traditional descriptors histograms of optical flow, our proposed SPHOG costs less computation resource. Vector of Locally Aggregated Descriptors (VLAD), which is a popular encoding approach for Bag-of-Feature representation. There is a main drawback of VLAD that it only considers the difference between local descriptor and their centroids. In order to resolve the weakness, we proposed a improved VLAD method called HOD-VLAD, which complementary the distribution information of local descriptors by computing a weight histograms of distance. We validated our proposed algorithm for human action recognition on three public available datasets KTH, UCF Sports and HMDB51. The evaluation experiment results indicate that the proposed descriptor and encoding method can improve the efficiency of human action recognition and the recognition accuracy.
机译：自动人体动作识别是用于视频监视和人体交互的系统的核心功能。在整个识别系统中，特征描述和编码代表两个关键的关键步骤。为了构建功能强大的动作识别框架，重要的是两个步骤必须提供可靠的性能。在本文中，我们提出了一种新的人类动作特征描述符，称为梯度时空直方图（SPHOG）。 SPHOG基于空间和时间推导信号，该信号提取连续帧之间的梯度变化。与传统的光流描述符直方图相比，我们提出的SPHOG花费更少的计算资源。局部聚集描述符向量（VLAD），这是功能包表示的一种流行编码方法。 VLAD的主要缺点是仅考虑局部描述符及其质心之间的差异。为了解决该缺点，我们提出了一种改进的VLAD方法，称为HOD-VLAD，该方法通过计算距离的权重直方图来补充局部描述符的分布信息。我们在三个公共可用数据集KTH，UCF Sports和HMDB51上验证了我们提出的用于人类动作识别的算法。评估实验结果表明，所提出的描述符和编码方法可以提高人体动作识别的效率和识别精度。

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