Pedestrian trajectory prediction based on temporal attention

Yuxin Wang; Xiuzhi Li; Zhenyu Jiao; Lei Zhang

doi:10.1117/12.2636485

6 May 2022 Pedestrian trajectory prediction based on temporal attention

Yuxin Wang, Xiuzhi Li, Zhenyu Jiao, Lei Zhang

Proceedings Volume 12176, International Conference on Algorithms, Microchips and Network Applications; 1217622 (2022) https://doi.org/10.1117/12.2636485
Event: International Conference on Algorithms, Microchips, and Network Applications 2022, 2022, Zhuhai, China

Abstract

Aiming at the problem that pedestrian trajectory prediction network based on Encoder-Decoder structure is easy to lose part of trajectory information in the process of long sequence encoding and decoding, Generative Adversarial Network based on Temporal Attention (TA) is proposed in this paper. It is used to assign influence weights to the trajectory information of the encoding and decoding layer, so that the model can make full use of the trajectory information useful for predicting the trajectory in the future and reduce the influence of redundant information. This paper adds a TA to the encoding and decoding layers of the Generative Adversarial Network, and trains it on the ETH and UCY datasets. Experimental results show that the proposed network has better prediction accuracy than existing methods.

Citation Download Citation

Yuxin Wang, Xiuzhi Li, Zhenyu Jiao, and Lei Zhang "Pedestrian trajectory prediction based on temporal attention", Proc. SPIE 12176, International Conference on Algorithms, Microchips and Network Applications, 1217622 (6 May 2022); https://doi.org/10.1117/12.2636485

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