A Comparative Study of a Real-Time Multi-Person Tracking System in an Urban Square Dataset

Real-time multi-object tracking plays a pivotal role in many computer vision applications such as spanning surveillance systems, crowd behavior analysis, and urban monitoring. Unlike existing datasets that predominantly focus on controlled environments or traffic scenarios, this paper introduces a novel, high-resolution dataset collected from Turku Market Square, Finland. The dataset captures diverse real-world conditions, including varying crowd densities, frequent occlusions, and dynamic pedestrian movements, providing a valuable benchmark for pedestrian tracking in unstructured urban environments. In addition to the dataset, we propose a real-time multi-object tracking system designed for object detection and tracking. Our system compares the performance of two well-known object detectors (YOLOv11 and Faster R-CNN) and evaluates their outputs using eight different tracking algorithms. The experimental results demonstrate that the proposed system achieves high precision scores of 0.88 and 0.87 for YOLO-based BoTSORT and ByteTrack, respectively. These findings underscore the effectiveness of deep learning-based tracking methods for realworld applications and establish a strong benchmark for future research in multi-person tracking within dynamic public spaces.