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Discriminative human action classification using locality-constrained linear coding

机译：使用局部约束线性编码的区分性人类行为分类

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摘要

We propose a Locality-constrained Linear Coding (LLC) based algorithm that captures discriminative information of human actions in spatio-temporal subsequences of videos. The input video is divided into equally spaced overlapping spatio-temporal subsequences. Each subsequence is further divided into blocks and then cells. The spatio-temporal information in each cell is represented by a Histogram of Oriented 3D Gradients (HOG3D). LLC is then used to encode each block. We show that LLC gives more stable and repetitive codes compared to the standard Sparse Coding. The final representation of a video sequence is obtained using logistic regression with l(2) regularization and classification is performed by a linear SVM. The proposed algorithm is applicable to conventional and depth videos. Experimental comparison with ten state-of-the-art methods on three depth video and two conventional video databases shows that the proposed method consistently achieves the best performance. (C) 2015 Elsevier B.V. All rights reserved.
机译：我们提出了一种基于位置约束的线性编码（LLC）的算法，该算法可捕获视频时空子序列中人类行为的判别信息。输入视频被分成等间隔的重叠时空子序列。每个子序列进一步划分为块，然后划分为单元。每个单元中的时空信息由定向3D梯度直方图（HOG3D）表示。然后，LLC用于编码每个块。我们证明，与标准稀疏编码相比，LLC提供了更稳定和重复的代码。视频序列的最终表示是使用具有1（2）正则化的逻辑回归获得的，并且通过线性SVM执行分类。该算法适用于常规视频和深度视频。在三个深度视频和两个常规视频数据库上与十个最新方法进行的实验比较表明，该方法始终可达到最佳性能。 （C）2015 Elsevier B.V.保留所有权利。

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