Parmarth Mundhra
Dept. Computer Engineering, Sinhgad College of Engineering, Pune
Krushna Dhobale
Dept. Computer Engineering, Sinhgad College of Engineering, Pune
Abhijit Deogire
Dept. Computer Engineering, Sinhgad College of Engineering, Pune
Rutuja Achole
Dept. Computer Engineering, Sinhgad College of Engineering, Pune
Nalini Mhetre
Dept. Computer Engineering, Sinhgad College of Engineering, Pune
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http://doi.org/10.37648/ijrst.v13i02.004
Recognizing human action has been one of the biggest challenges in computer vision for the past two decades. Recently, it has become feasible to extract precise and cost-effective skeleton information. Our proposed system utilizes a cut-based framework to identify human actions using skeleton data. By using a single stationary camera as input, this system can recognize various continuous human activities in real-time, including raising or waving one or more hands, sitting down, and bending over. The recognition process is based on machine learning. Firstly, a dataset with the human body's coordinates is created. Then, a training model is developed using Logistic Regression and an outcome to be achieved. Finally, the model is utilized to identify human activities such as sitting, running, and waking up, as well as recognizing suspicious behaviour.
Keywords: Human Activity; Human Coordinates; Logistic Regression; Machine Learning; Random Forest Classifier; Suspicious Behaviour
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