Deep Learning
The project aims to perform human action recognition on images and evaluate the accuracy and performance of the implemented methods. Using YOLOv5 for object detection and a fine-tuned ResNet50 for feature extraction and classification, the system identifies and labels human actions captured in static images. This approach enables the recognition of actions such as running, jumping, or sitting. The project not only focuses on achieving accurate recognition but also addresses challenges like pose variations, occlusions, and diverse environments to enhance the model's robustness and reliability.
Basic Info:
- Team: Haoyu Yang, Ruijie Sun