Precise Human Activity Recognition using Convolutional Neural Network and Deep Learning Models - Patil, Harshavardhan; Malkhede, Priti; Madake, Shreyash; Kokate, Ashutosh; Bhandure, Yash (International Journal of Computer Sciences and Engineering, 2024)
Human Activity Recognition (HAR) plays a pivotal role in various domains, ranging from healthcare to surveillance and robotics. This paper offers a comprehensive detail of Convolutional Neural Network (CNN)-based methodologies in HAR, emphasizing their efficiency in accurately recognizing human activities from video data. We used the UCF50 dataset, which contains videos of 50 different human activities, making it a suitable benchmark for evaluating CNN-based HAR models. The study investigates the utilization of CNNs for feature extraction and classification in HAR, focusing on techniques such as frame extraction, data preprocessing, and model architectures. Detailed analysis of convolutional layers, pooling layers, and activation functions within CNNs showcases their ability to capture intricate spatial and temporal features. The research also delves into the benefits of data augmentation and normalization in enhancing model performance and generalization. The findings highlight the significant advantages of CNNs in capturing spatial information and improving accuracy in HAR tasks, making them highly effective for real-world applications across various domains.