Histogram of Accumulated Changing Gradient Orientation (HACGO) for saliency navigated action recognition
Histogram of Accumulated Changing Gradient Orientation (HACGO) for saliency navigated action recognition
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DOI：
10.1109/snpd.2017.8022726
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发表时间：
2017-06
期刊：
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通讯作者：
Hnin Mya Aye;Sai Maung Maung Zaw-Sai-Maung-Maung-Zaw-19312689
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文献类型：
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作者：
Hnin Mya Aye;Sai Maung Maung Zaw-Sai-Maung-Maung-Zaw-19312689
动作识别是近年来计算机视觉领域的一个活跃研究领域。然而，由于背景杂波、光照变化、类内变化大和噪声等因素造成的困难，这仍然是一项具有挑战性的任务。在本文中，我们的目标是通过使用显著检测来导航注意焦点(动作区域)，并引入一种特征描述符，即累积变化梯度方向直方图(HACGO)来实现动作识别。我们首先通过计算模式和颜色的清晰度来检测每一帧视频中的显著程度，从而定位出动作区域。然后，利用改进的HACGO算法和现有的HOG和HOF特征描述符来提取外观和运动特征。最后，应用多类支持向量机分类器对不同的动作进行识别。这些实验是在标准的UCF体育动作数据集上进行的。实验结果表明，通过一种新的特征描述符组合，我们的动作识别方法获得了较高的识别准确率。
Action recognition has been an active research area in computer vision community during the recent years. However, it is still a challenging task due to the difficulties mainly resulted from the background clutter, illumination changes, large intra-class variation and noise. In this paper, we aim to develop an action recognition approach by navigating focus of attention (action region) with saliency detection and introducing a feature descriptor, namely Histogram of Accumulated Changing Gradient Orientation (HACGO). We firstly detect saliency in each video frame by computing pattern and color distinctness to localize action region. Then, we extract appearance and motion features using proposed HACGO, and existing HOG and HOF feature descriptors. Finally, a multi-class SVM classifier is applied to recognize different actions. The experiments were conducted on the standard UCF Sports action dataset. As experimental results, our action recognition approach achieved high recognition accuracy with a new combination of feature descriptors.