AI‐Powered Human Activity Detection and Tracking in Dense Crowds Using YOLOv8‐DeepSORT

ABSTRACT Many organisations face challenges in accurately and efficiently identifying human activities using manually operated CCTV cameras and small datasets, particularly in densely populated and dynamic environments. This paper presents a real‐time human activity detection and recognition system that leverages You Only Look Once version 8 (YOLOv8) for object detection and integrates the deep simple online real‐time tracking algorithm for associate frames, robust tracking and individual identity preservation. Evaluated on a combination of public and experimental datasets containing over 10,000 frames, the system achieves notable enhancements in efficiency along with accuracy. Through data preprocessing and algorithm optimisation, the Adam optimiser outperformed stochastic gradient descent (SGD) by more than 5% in accuracy. K ‐fold cross‐validation was applied to reduce overfitting, while anchor boxes of varying scales and aspect ratios improved the detection of objects of different sizes. The aspect ratio, which is used to mitigate the effect of object size and control edge‐orientation issues by computing the ratio of width to height of the bounding boxes in YOLOv8, plays a crucial role in object detection.YOLOv8l, combined with DeepSORT, delivers state‐of‐the‐art results, achieving a mean average precision of 96.1% in mixed datasets. The system assigns unique IDs to track individuals, enabling precise frame association during motion in crowded areas, which is crucial for maintaining accuracy in complex environments. This study presents a robust and scalable model, offering a significant improvement for public surveillance technology.