Identification of Human Activity by Utilizing YOLOv5s Approaches

The significance of identifying human activity for the purpose of ensuring security is progressively growing day by day. Human activity recognition is an innovative technology that detects and recognizes human actions inside video footage. This research involves performing deep learning based YOLOv5s algorithms for the detection and recognition of four types of human activity, like sitting, standing, running, and sleeping. This research uses its own data set, which contains a total of 2375 images. There are 1745 images used for training and 630 images used for validation. We use Makesense. AI to classify the datasets. After classifying, we use the YOLOv5s method to train it. For this research, we utilized the Google Colabatory Environment with a V100 GPU. The results of this research indicate a precision score of 95.6%, a recall value of 95.8%, a f1-score of 95.69%, and an accuracy of 97%. The results of the experiment demonstrate that the model is capable of accurately identifying human activity. The model's capability to accurately identify human activity enables its utilization for monitoring purposes in various institutions such as daycare centers, schools, colleges, and hospitals.