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Article · Sustainability · September 3, 2026

AH-YOLOFlow: Vision-Based Pedestrian Flow Analytics for Smart Urban Crosswalk Performance Monitoring

Farkhod Akhmedov, Doston Khasanov, Sarvarbek Sodikovich Yusupov, Oybek Mallayev, Halimjon Khujamatov, Toshtemir Abdikhafizovich Khujakulov

Doston Khasanov
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Abstract

Proper management of pedestrian flow at urban crosswalks is crucial for alleviating congestion, reducing waiting times, and improving general traffic safety. This paper presents AH-YOLOFlow, a computer vision framework for pedestrian detection, multi-object tracking, and zone-based flow analytics at urban crosswalks. The proposed framework integrates an attention-enhanced YOLO26n detector, Hungarian assignment-based multi-object tracking, polygon-based spatial zone modeling, and key performance indicator aggregation to estimate pedestrian flow rate, waiting behavior, crossing activity, and localized pedestrian density. The framework was evaluated using a 228.74-s CCTV crosswalk video with a fixed split and an augmented training dataset. Experimental results demonstrate that the proposed detector achieved a mAP@50-95 of 0.5794, mAP@50 of 0.8000, precision of 0.9003, and recall of 0.6847, outperforming the evaluated YOLO-family baselines in terms of mAP@50-95 and precision while remaining competitive with RT-DETR. In the end-to-end pedestrian analytics evaluation, AH-YOLOFlow identified 638 zone-entry events and 495 directional movement events, and successfully associated 54.01% of tracked pedestrian identities with predefined spatial regions. Furthermore, a pseudo-MOT evaluation based on spatially propagated identity annotations demonstrated enhanced tracking consistency following data augmentation, achieving a pseudo-MOTA of 0.7671, pseudo-MOTP of 0.9205, pseudo-HOTA of 0.4527, and pseudo-IDF1 of 0.3844. These findings demonstrate that AH-YOLOFlow provides an effective and computationally efficient framework for real-time crosswalk monitoring by integrating pedestrian detection, tracking, and spatial analytics. In future work, we will focus on validating the system across diverse urban environments using larger multi-scene datasets with manually verified identity annotations to further improve tracking robustness and generalization.

Video Surveillance and Tracking MethodsAdvanced Neural Network ApplicationsEvacuation and Crowd Dynamicspedestrian detectionmulti-target trackingpedestrian density estimation

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