Real-Time Human Action Recognition Using Deep Learning

Real-Time Human Action Recognition Using Deep Learning

Houssem Eddine Azzag (École supérieure en informatique de Sidi Bel Abbès, Algeria), Imed Eddine Zeroual (Université Mohamed-Chérif Messaadia, Souk Ahras, Algeria), and Ammar Ladjailia (Université Mohamed-Chérif Messaadia, Souk Ahras, Algeria)
Copyright: © 2022 |Pages: 10
DOI: 10.4018/IJAEC.315633
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The future of computer vision lies in deep learning to develop machines to solve our human problems. One of the most important areas of research is smart video surveillance. This feature is related to the study and recognition of movements, and it's used in many fields, like security, sports, medicine, and a whole lot of new applications. The study and analysis of human activity is very important to improve because it is a very sensitive field, like in security, the human needs a machine's help a lot; and in recent years, developers have adopted many advanced algorithms to discover the type of movements humans preform, and the results differ from one to another. The most important part of human activity recognition is real time, so one can detect any issue, like a medical problem, in time. In this regard, the authors will use methods of deep learning to reach a good result of recognition of the nature of human action in real time clips.
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Researchers have devoted a lot of efforts in the field of artificial intelligence that studies the recognition of human activity through videos, and in recent decades they have found many different ways (Lei 2019), (Yadav 2019) (Figure 1) to improve prediction results in terms of motion recognition accuracy and recognition speed in live video clips. Some clip-to- skeleton techniques were used (Parisi 2020) to discover differences by creating a skeletal model that simulatesthe movements in the original video. The differences are studied for the structural model and the prediction results are given for the original video. While others used special techniques represented in identifying each section of the body gradually, and this helps many people to get good accuracy, many researchers use a method based on these RGBD colors to color some parts of the body with different colors (Luvizon 2019), (Liu 2020) and use them to detect activity by color (Khowaja 2020). But the best way is to use the person as a shadow without any background or effects (Gnouma 2019); some other solutions are based on placing the skeleton in Space-Time and doing a 3D tracking (Yadav 2019). So, we can say this filed is missing two points: the first one, most of the old research is not in real-time, and the second one is the accuracy of the work according to all these results. It’s time to give our way to improve those problems, to get a real-time recognition with high accuracy.

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