A review of pedestrian trajectory tracking based on deep learning

Ruixue Yu; Zhaoguo Zhang; Yi Lu; Wei Song; Xingguo Qin; Yi Ning; Jinlong Chen

doi:10.1117/12.3035210

19 July 2024 A review of pedestrian trajectory tracking based on deep learning

Ruixue Yu, Zhaoguo Zhang, Yi Lu, Wei Song, Xingguo Qin, Yi Ning, Jinlong Chen

Proceedings Volume 13213, International Conference on Image Processing and Artificial Intelligence (ICIPAl 2024); 132132Y (2024) https://doi.org/10.1117/12.3035210
Event: International Conference on Image Processing and Artificial Intelligence (ICIPAl2024), 2024, Suzhou, China

Abstract

Given the rapid advancement of deep learning in recent years, pedestrian trajectory tracking technology has emerged. Target tracking is used to continuously track detection objects in video sequences to obtain information such as motion trajectories and positions. For pedestrian trajectory tracking, this article introduces multi-target tracking methods based on convolutional neural networks and Transformer, summarizes the improvement points of various algorithms of "one-step method", "two-step method" and Transformer, and organizes the largest pedestrian tracking data set MOT Challenge, evaluation indicators and experimental results point out existing problems in trajectory tracking and look forward to the development direction.

(2024) Published by SPIE. Downloading of the abstract is permitted for personal use only.

Citation Download Citation

Ruixue Yu, Zhaoguo Zhang, Yi Lu, Wei Song, Xingguo Qin, Yi Ning, and Jinlong Chen "A review of pedestrian trajectory tracking based on deep learning", Proc. SPIE 13213, International Conference on Image Processing and Artificial Intelligence (ICIPAl 2024), 132132Y (19 July 2024); https://doi.org/10.1117/12.3035210

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