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Human Actions Tracking and Recognition Based on Body Parts Detection via Artificial Neural Network

机译：基于人工神经网络的人体部位检测对人体动作的跟踪与识别

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Human body action recognition has drawn a good deal of interest in the community of computer vision, owing to its wide range of applications. Recently, the video / image sequence base action recognition techniques are believed to be ideal for its efficiency and lower cost compared to other techniques such as the ambient sensor and the wearable sensor. However, given to a large amount of variation in human pose and image quality, reliable detection of human action is still a very challenging job for scientists. In this document, we used linear discriminant analysis for the generation of features from the body parts detected. The primary goal of this study is to combine linear discriminant analysis with an artificial neural network for precise human action detection and recognition. Our proposed mechanism detects complicated human actions in two state-of-the-art datasets, i.e. KTH-dataset and Weizmann Human Action. We obtained multidimensional features from twelve body parts, which are estimated from body models. These multidimensional characteristics are used as inputs for the artificial neural network. To access the efficiency of our suggested method, we compared the outcomes with other state-of-the-art classifiers. Experimental results show that our proposed technique is reliable and applicable in health exercise systems, smart surveillance, e-learning, abnormal behavioral detection, protection for child abuse, care of the elderly people, virtual reality, intelligent image retrieval and human computer interaction.
机译：由于其广泛的应用，人体动作识别引起了计算机视觉界的极大兴趣。近来，与基于环境的传感器和可穿戴式传感器的其他技术相比，基于视频/图像序列的基础动作识别技术被认为对于其效率和较低的成本而言是理想的。但是，考虑到人体姿势和图像质量的巨大差异，对人类行为的可靠检测对于科学家而言仍然是一项非常具有挑战性的工作。在本文中，我们使用线性判别分析从检测到的身体部位生成特征。这项研究的主要目标是将线性判别分析与人工神经网络相结合，以进行精确的人体动作检测和识别。我们提出的机制可以在两个最新的数据集中检测复杂的人类行为，即KTH数据集和Weizmann人类行为。我们从身体模型估计的十二个身体部位中获得了多维特征。这些多维特征被用作人工神经网络的输入。为了获得建议方法的效率，我们将结果与其他最新分类器进行了比较。实验结果表明，本文提出的技术是可靠的，适用于健康锻炼系统，智能监控，电子学习，异常行为检测，儿童虐待保护，老人护理，虚拟现实，智能图像检索和人机交互。

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