Activity recognition, the task of identifying and classifying human activities based on sensor data, has gained significant attention in recent years due to its potential applications in various domains, including surveillance, healthcare, and human-computer interaction. In particular, multi-view activity recognition, which utilizes data from multiple sensors or viewpoints, has emerged as a promising approach to improve accuracy and robustness.
This research focuses on the development and application of intelligent computational techniques for multi-view activity recognition. Intelligent techniques encompass a range of algorithms and models that leverage machine learning, deep learning, and artificial intelligence to automatically extract meaningful features and patterns from sensor data. These techniques enable the system to learn and recognize complex activities with high accuracy and adaptability.
By incorporating multiple viewpoints or sensor modalities, such as video cameras, depth sensors, and wearable devices, the system can capture a more comprehensive representation of human activities. This holistic approach enhances the system's ability to handle occlusions, variations in appearance, and complex activity scenarios.
The research aims to address several key challenges in multi-view activity recognition, including feature fusion, temporal modeling, and scalability. It explores innovative methodologies to combine information from multiple views effectively, capture temporal dependencies, and scale up the system to handle large datasets or real-time applications.
The proposed intelligent techniques for multi-view activity recognition have the potential to revolutionize various domains, including smart homes, video surveillance, sports analysis, and healthcare monitoring. By accurately recognizing and understanding human activities, these techniques can enable intelligent systems to provide personalized services, enhance safety and security, and improve overall user experiences
