Design and implementation of pedestrian detection system based on video image

Jinhui Zhang

doi:10.1117/12.2673341

14 April 2023 Design and implementation of pedestrian detection system based on video image

Jinhui Zhang

Proceedings Volume 12613, International Conference on Computer Vision, Application, and Algorithm (CVAA 2022); 1261308 (2023) https://doi.org/10.1117/12.2673341
Event: International Conference on Computer Vision, Application, and Algorithm (CVAA 2022), 2022, Chongqing, China

Abstract

Facing the problems of low working efficiency, poor accuracy and insufficient stability of traditional motion detection algorithms and machine learning algorithms in pedestrian detection, this paper will take artificial intelligence technology as the core, adopt R-CNN series deep learning models, combine Support Vector Machine (SVM) classifier, and rely on TensorFlow deep learning framework to realize pedestrian detection and recognition in video and picture files with the support of OpengCV Open Source Visual Library. Combined with ASP.NET framework, C# language is used to complete the design and development of each functional module, improve the deployment of the corresponding API interface, and build a front-end interactive interface to form a pedestrian detection system based on Web. The overall design of the system chooses B/S architecture, which allows users to access the Web Server through simple request operation to complete the detection of video image data in the database and return the detection results to the front page for display. The system will greatly improve the precision and accuracy of pedestrian detection and make a positive attempt to further expand the application scenarios of pedestrian detection in video images.

Citation Download Citation

Jinhui Zhang "Design and implementation of pedestrian detection system based on video image", Proc. SPIE 12613, International Conference on Computer Vision, Application, and Algorithm (CVAA 2022), 1261308 (14 April 2023); https://doi.org/10.1117/12.2673341

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