Enhanced Human-Computer Interaction: A Unified Pipeline for Classification and Gesture Analysis

The purpose of this study is to develop a unified framework that combines object classification with vision-based gesture recognition. The proposed approach integrates YOLOv3 object detection enhanced by Z-Score Propensity Normalization to minimize false positives in Non-Maximum Suppression. Gesture recognition is performed using geometric contour detection and a Support Vector Machine classifier trained with Principal Component Analysis, which hierarchically refines detected bounding boxes and classifies hand gestures using spatial-temporal distance metrics. Experimental results show an average accuracy of 96.70%, a precision of 0.968, and an F1-score of 0.9671 for recognizing three gestures: hands down, one hand up, and hands up. This integrated method significantly improves computational efficiency and robustness, demonstrating strong potential for practical applications in augmented reality, assistive technologies, and immersive computing.