Vision-Based People Counting and Tracking for Urban Environments
Daniyar Nurseitov, Kairat Bostanbekov, Nazgul Toiganbayeva, Aidana Zhalgas, Didar Yedilkhan, Beibut Amirgaliyev

TL;DR
This paper introduces a computer vision system for accurately counting and tracking people in urban transport settings using deep learning.
Contribution
The paper presents a modified DeepSORT tracking pipeline and a unified architecture for detection, tracking, and event logging in dense urban environments.
Findings
The proposed system achieved 92% detection accuracy and 85% counting accuracy using a new dataset of 4047 images.
YOLOv8 outperformed Mask R-CNN and DETR in speed, accuracy, and computational efficiency.
The system generates annotated video streams and event logs, offering a scalable alternative to traditional passenger counting methods.
Abstract
Population growth and expansion of urban areas increase the need for the introduction of intelligent passenger traffic monitoring systems. Accurate estimation of the number of passengers is an important condition for improving the efficiency, safety and quality of transport services. This paper proposes an approach to the automatic detection and counting of people using computer vision and deep learning methods. While YOLOv8 and DeepSORT have been widely explored individually, our contribution lies in a task-specific modification of the DeepSORT tracking pipeline, optimized for dense passenger environments, strong occlusions, and dynamic lighting, as well as in a unified architecture that integrates detection, tracking, and automatic event-log generation. Our new proprietary dataset of 4047 images and 8918 labeled objects has achieved 92% detection accuracy and 85% counting accuracy,…
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Taxonomy
TopicsVideo Surveillance and Tracking Methods · Advanced Neural Network Applications · Autonomous Vehicle Technology and Safety
