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Spatio-temporal action localization and detection for human recognition in big dataset

机译：大数据集中人为识别的时空行为定位与检测

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摘要

Human action recognition is still attracting the computer vision research community due to its various applications. However, despite the variety of methods proposed to solve this problem, some issues still need to be addressed. In this paper, we present a human action detection and recognition process on large datasets based on Interest Points trajectories. In order to detect moving humans in moving field of views, a spatio-temporal action detection is performed basing on optical flow and dense speed-up-robust features (SURF). Then, a video description based on a fusion process that combines motion, trajectory and visual descriptors is proposed. Features within each bounding box are extracted by exploiting the bag-of-words approach. Finally, a support-vector-machine is employed to classify the detected actions. Experimental results on the complex benchmark UCF101, KTH and HMDB51 datasets reveal that the proposed technique achieves better performances compared to some of the existing state-of-the-art action recognition approaches. (C) 2016 Elsevier Inc. All rights reserved.
机译：由于人类动作识别的各种应用，它仍吸引着计算机视觉研究界。然而，尽管提出了解决该问题的多种方法，但是仍然需要解决一些问题。在本文中，我们提出了基于兴趣点轨迹的大型数据集上的人类动作检测和识别过程。为了在运动的视野中检测运动的人，基于光流和密集的加速健壮特征（SURF）进行时空行为检测。然后，提出了一种基于融合了运动，轨迹和视觉描述符的融合过程的视频描述。利用词袋法提取每个边界框中的要素。最后，采用支持向量机对检测到的动作进行分类。在复杂基准UCF101，KTH和HMDB51数据集上的实验结果表明，与某些现有的最新动作识别方法相比，该技术具有更好的性能。 （C）2016 Elsevier Inc.保留所有权利。

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