Early intention prediction of pedestrians using contextual attention-based LSTM-Reference-Cited by-同舟云学术

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Early intention prediction of pedestrians using contextual attention-based LSTM

Published:2022-10-05 Issue:10 Volume:82 Page:14713-14729
ISSN:1380-7501
Container-title:Multimedia Tools and Applications
language:en
Short-container-title:Multimed Tools Appl

Author:

Lian Jing,Yu Fengning,Li Linhui,Zhou Yafu

Funder

Innovative Research Group Project of the National Natural Science Foundation of China

the China Fundamental Research Funds for the Central Universities

Science and Technology Innovation Fund of Dalian

Publisher

Springer Science and Business Media LLC

Subject

Computer Networks and Communications,Hardware and Architecture,Media Technology,Software

Link

https://link.springer.com/content/pdf/10.1007/s11042-022-13814-y.pdf

Reference30 articles.

1. Aboah A (2021) A vision-based system for traffic anomaly detection using deep learning and decision trees[C]. Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, pp 4207–4212

2. Bandyopadhyay T, Jie CZ, Hsu D et al (2013) Intention-aware pedestrian avoidance[C]. Experimental robotics. Springer, Heidelberg, pp 963–977

3. Cao Z, Simon T, Wei SE et al (2017) Realtime multi-person 2d pose estimation using part affinity fields[C]. Proceedings of the IEEE conference on computer vision and pattern recognition, pp 7291–7299

4. Chaabane M, Trabelsi A, Blanchard N et al (2020) Looking ahead: anticipating pedestrians crossing with future frames prediction[C]. Proceedings of the IEEE/CVF Winter Conference on Applications of Computer Vision, pp 2297–2306

5. Chen T, Tian R, Ding Z (2021) Visual reasoning using graph convolutional networks for predicting pedestrian crossing intention[C]. Proceedings of the IEEE/CVF International Conference on Computer Vision, pp 3103–3109

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1. Pedestrian Safety by Intent Prediction: A Lightweight LSTM-Attention Architecture and Experimental Evaluations with Real-World Datasets;2024 IEEE Intelligent Vehicles Symposium (IV);2024-06-02

2. A Review of Decision-Making and Planning for Autonomous Vehicles in Intersection Environments;World Electric Vehicle Journal;2024-03-06

3. Pedestrian and vehicle behaviour prediction in autonomous vehicle system — A review;Expert Systems with Applications;2024-03

4. Multimodal-Based Pedestrian Crossing Intention Recognition Method*;2023 China Automation Congress (CAC);2023-11-17

5. RLSTM: A Novel Residual and Recurrent Network for Pedestrian Action Classification;Computer Analysis of Images and Patterns;2023

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