论文标题
使用局部两流卷积神经网络特征和支持向量机的人类行动识别
Human Action Recognition using Local Two-Stream Convolution Neural Network Features and Support Vector Machines
论文作者
论文摘要
本文提出了一种简单而有效的方法，用于视频中的人类行动识别。所提出的方法分别使用视频的采样片段中使用最先进的三维卷积神经网络提取本地外观和运动特征。然后将这些局部功能连接为形成全局表示形式，然后将其用于训练线性SVM，以使用视频的完整上下文（作为先前工作中使用的部分上下文）执行动作分类。这些视频经历了两种简单的预处理技术，光流缩放和作物填充。我们对三个通用基准数据集进行了广泛的评估，以凭经验显示SVM的好处以及两个预处理步骤。
This paper proposes a simple yet effective method for human action recognition in video. The proposed method separately extracts local appearance and motion features using state-of-the-art three-dimensional convolutional neural networks from sampled snippets of a video. These local features are then concatenated to form global representations which are then used to train a linear SVM to perform the action classification using full context of the video, as partial context as used in previous works. The videos undergo two simple proposed preprocessing techniques, optical flow scaling and crop filling. We perform an extensive evaluation on three common benchmark dataset to empirically show the benefit of the SVM, and the two preprocessing steps.
