Video-based human activity recognition analysis using YOLOv26

Abstract

Human Activity Recognition (HAR) is a rapidly growing research field in computer vision and has various applications, such as intelligent surveillance systems, sports analysis, healthcare, and human-computer interaction. This study aims to analyze the performance of the YOLOv26 Classification model in recognizing video-based human activities using the UCF101 dataset. This study uses six activity classes, namely JumpingJack, Punch, PushUps, Typing, WalkingWithDog, and WritingOnBoard. The research method is carried out through video frame extraction using a uniform sampling technique, the formation of an image classification dataset, YOLOv26 model training, and evaluation at the frame and video levels. Experiments were conducted using 36 configuration combinations consisting of four YOLOv26 model variants (YOLOv26-n, YOLOv26-s, YOLOv26-m, and YOLOv26-l), three variations in the number of frames (8, 16, and 24 frames), and three variations in the number of epochs (50, 100, and 150 epochs). Video evaluation was conducted using a majority voting approach with accuracy, precision, recall, and F1-score metrics. The results showed that all configurations produced video accuracy above 93%. The best configuration was obtained with the YOLOv26-m model with 16 frames and 50 epochs, which achieved video accuracy of 98.26%, precision of 98.15%, recall of 98.37%, and F1-score of 98.23%. Confusion matrix analysis showed that most predictions were on the main diagonal, indicating the model's ability to distinguish human activities with a low error rate. These results prove that YOLOv26 Classification has excellent performance for video-based human activity recognition and has the potential to be applied to various applications based on automatic human activity analysis.