期刊
IEEE TRANSACTIONS ON INTELLIGENT TRANSPORTATION SYSTEMS
卷 23, 期 6, 页码 5298-5313出版社
IEEE-INST ELECTRICAL ELECTRONICS ENGINEERS INC
DOI: 10.1109/TITS.2021.3052908
关键词
Task analysis; Predictive models; Cameras; Trajectory; Legged locomotion; Training; Adaptation models; Pedestrian motion trajectory prediction; auto motion; deep learning; circular training
资金
- National Key Research and Development Program of China [2018YFB0105000]
- National Natural Science Foundation of China [U20A20333, 52072160, 51875255, U1764264]
- Natural Science Foundation of Jiangsu Province [BK20180100]
- Key Research and Development Program of Jiangsu Province [BE2019010-2, BE2020083-3]
- Jiangsu Province's six talent peaks [TD-GDZB-022]
- Australia ARC DECRA [DE190100931]
The researchers proposed a deep learning model for predicting pedestrian motion trajectory from far shot first-person perspective video and achieved state-of-the-art results. The model includes four key innovations, such as a macroscopic prediction module, a relative motion transformation module, a circular training module, and a specific dataset, which enhanced prediction accuracy.
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.
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