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Real-time vehicle pedestrian detection and tracking algorithm based on computer vision

Published: 20 September 2024 Publication History

Abstract

Detection and tracking of vehicle pedestrians have important application values in the fields of intelligent driving and traffic monitoring. To this end, the study improves the YOLOv5s algorithm by replacing the backbone network of YOLOv5s with SGWin Transformer V2, and introduces the CBAM module, while optimizing the SIoU loss function to obtain the improved YOLOv5s algorithm. The improved YOLOv5s algorithm is used for the detection of vehicles and pedestrians in real-time video, then the fusion model is used to correlate the motion trajectories of the detected targets, and finally the Kalman filter tracking algorithm is applied to correct the tracking prediction results to realize the fast, accurate and continuous detection and tracking of vehicles and pedestrians. The results show that the tracking and detection accuracy of the method used in the study is 84.7%, and 51 vehicles and 32 pedestrians are accurately labeled. Research algorithms can accurately achieve vehicle and pedestrian recognition in complex road environments, and provide technical guidance for object recognition of the same type.

References

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FAIML '24: Proceedings of the 2024 3rd International Conference on Frontiers of Artificial Intelligence and Machine Learning
April 2024
379 pages
ISBN:9798400709777
DOI:10.1145/3653644
Permission to make digital or hard copies of all or part of this work for personal or classroom use is granted without fee provided that copies are not made or distributed for profit or commercial advantage and that copies bear this notice and the full citation on the first page. Copyrights for components of this work owned by others than the author(s) must be honored. Abstracting with credit is permitted. To copy otherwise, or republish, to post on servers or to redistribute to lists, requires prior specific permission and/or a fee. Request permissions from [email protected].

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Association for Computing Machinery

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Published: 20 September 2024

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