Citation

Cai Y, Dai L, Wang H, Chen L, Li Y, Sotelo MA, Li Z. IEEE Trans. Intel. Transp. Syst. 2022; 23(6): 5298-5313.

Copyright

(Copyright © 2022, IEEE (Institute of Electrical and Electronics Engineers))

DOI

10.1109/TITS.2021.3052908

PMID

unavailable

Abstract

Pedestrian motion trajectory prediction is an important task in intelligent driving, and it can provide a valuable reference for the subsequent path decision of intelligent driving. However, so far, there are only a few models in the field of specific pedestrian motion track prediction in intelligent driving from far shot first-person perspective video. To accomplish this task, we proposed a deep learning model for pedestrian motion trajectory prediction from far shot first-person perspective video with four key innovations: a) A macroscopic pedestrian trajectory prediction module is established under the close correlation between neighboring frames to estimate the pedestrian motion track on the whole; b) A relative motion transformation module of vehicle-mounted camera is designed to consider the effect of vehicle-mounted camera's ego-motion on the pedestrian motion track; c) We set up a circular training module to maintain the number of parameters in our model to simplify and reduce the size of model; d) A new far shot first-person pedestrian motion dataset under intelligent driving is specifically established to train and test the proposed model. The above four modules are integrated into the proposed deep learning model, which achieves state-of-the-art results for predicting pedestrian motion trajectory from both far and close shot first-person perspective video.

Language: en

Keywords

Cameras; Training; Trajectory; deep learning; Task analysis; Predictive models; Legged locomotion; Adaptation models; auto motion; circular training; Pedestrian motion trajectory prediction