Real-Time Human Assault Prediction Using Human Behavior Recognition

Partial blockage, perspective, size variations, fore-ground chaos, illumination and appearance oscillations are some of the hurdles that make it problematic to deduce human behavior from progression or still photographs. Thus, the problem of classifying time series for Human Behavior Recognition is very difficult. It is the use of sensor data to infer individuals or circumstances. In order to properly create features out of raw data to fit a model using machine learning, the vast majority of pattern recognition techniques require in-depth domain expertise and certain signal processing techniques. The basis of human- computer and interaction between people is the recognition of pattern behavior. Systems that are operated manually require a lot more time and money. The current state of the world has made the construction of human behavior recognition systems necessary. Not only will this system be affordable, but it's also a utility-based system that could be integrated into several apps that help with various tasks requiring accurate and efficient recognition at a high rate of time savings. The KTH action video dataset is used in the present project's system design. Preprocessing techniques make it possible to retrieve those preselected specific frames. Pixel and visual flow discovery techniques are employed in the feature gathering processing process. The extraction of attributes is visualized using the data visual representation approach. To identify and categorize human behaviors, deep learning algorithms like Spatio-Temporal Net are applied after this technique. This well organised model can be used as an application for monitoring of roads, e.g., a recording device module installed in the road that is always monitoring the area. The camera alerts the authorities when it detects odd behavior in people, such as fighting or other conflicts. The police have developed a React Native mobile application that triggers alert notifications and enables live streaming upon detecting unusual human actions via the camera. As a result, this research successfully equips the cameras with powered by AI human activity identification models, which can be used in actual-time applications like preventing and generating proof of aberrant detection on a road.