Real-Time Jaywalking Detection and Notification System using Deep Learning and Multi-Object Tracking | IEEE Conference Publication | IEEE Xplore

Real-Time Jaywalking Detection and Notification System using Deep Learning and Multi-Object Tracking


Abstract:

Jaywalking refers to pedestrians walking or crossing in a roadway that is not dedicated to pedestrians. Due to illegal jaywalking, every year a lot of accidents happen wo...Show More

Abstract:

Jaywalking refers to pedestrians walking or crossing in a roadway that is not dedicated to pedestrians. Due to illegal jaywalking, every year a lot of accidents happen worldwide that cause a significant amount of death and other physical injuries. Real-time jaywalking detection and notification systems can contribute to protecting vulnerable road users and increasing road safety. Many computer vision-based image processing techniques have been proposed to detect jaywalking including deep learning, motion path analysis, motion object segmentation, trajectory forecasting and position localization. However, these techniques are designed and evaluated for a single road area and have limited notification capability. In this paper, we propose a real-time multi-object tracking approach for jaywalking detection and notification that can be applied in multiple road areas simultaneously. We use the state-of-the-art deep learning model YOLOv4 and the multi-object tracking algorithm DeepSORT for real-time object detection and tracking, respectively. The notification component incorporates a novel vehicle-region pair matching algorithm based on the proximity of vehicles to the monitored region. Performance evaluation shows that our proposed approach can effectively detect jaywalking with 100% accuracy and provide push notifications to nearby vehicles in real-time.
Date of Conference: 04-08 December 2022
Date Added to IEEE Xplore: 11 January 2023
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Conference Location: Rio de Janeiro, Brazil

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