Faster R-CNN-based pedestrian detection and tracking

Linke Wang

doi:10.1117/12.3006095

10 October 2023 Faster R-CNN-based pedestrian detection and tracking

Linke Wang

Proceedings Volume 12799, Third International Conference on Advanced Algorithms and Signal Image Processing (AASIP 2023); 1279956 (2023) https://doi.org/10.1117/12.3006095
Event: 3rd International Conference on Advanced Algorithms and Signal Image Processing (AASIP 2023), 2023, Kuala Lumpur, Malaysia

Abstract

Pedestrian detection and tracking is an important research area in the field of computer vision. It has extensive application in many areas, such as intelligent transportation,intelligent video surveillance. This paper uses Fast region-based convolutional neural network( Fast RCNN) for reference，propose pedestrian detection method based on Faster R-CNN in target recognition field. Image features are extracted by CNN. A region proposal network (RPN) is built up to extract regions that might contain pedestrians. And the region is identified and classified by detection network.According to the Kalman filter and Camshift tracking algorithm, this paper proposes the Camshift algorithm based on Kalman filter to track the detected pedestrians. Experimental results show that Faster R-CNN model and Camshift algorithm based on Kalman filter can be used to detect and track pedestrians in surveillance video.

(2023) Published by SPIE. Downloading of the abstract is permitted for personal use only.

Citation Download Citation

Linke Wang "Faster R-CNN-based pedestrian detection and tracking", Proc. SPIE 12799, Third International Conference on Advanced Algorithms and Signal Image Processing (AASIP 2023), 1279956 (10 October 2023); https://doi.org/10.1117/12.3006095

ACCESS THE FULL ARTICLE

INSTITUTIONAL
Select your institution to access the SPIE Digital Library.

SELECT YOUR INSTITUTION

PERSONAL
Sign in with your SPIE account to access your personal subscriptions or to use specific features such as save to my library, sign up for alerts, save searches, etc.

PERSONAL SIGN IN

No SPIE Account? Create one

PURCHASE THIS CONTENT

SUBSCRIBE TO DIGITAL LIBRARY

50 downloads per 1-year subscription

Members: $195

Non-members: $335 ADD TO CART

25 downloads per 1 - year subscription

Members: $145

Non-members: $250 ADD TO CART

PURCHASE SINGLE ARTICLE

Includes PDF, HTML & Video, when available