Pedestrian trajectory prediction via the Social‐Grid LSTM model
Author:
Affiliation:
1. College of Intelligence Science and Technology, National University of Defense TechnologyChangshaPeople's Republic of China
2. Department of Mechatronics EngineeringChungnam National UniversityDaejeonRepublic of Korea
Funder
National Natural Science Foundation of China
Publisher
Institution of Engineering and Technology (IET)
Subject
General Engineering,Energy Engineering and Power Technology,Software
Link
https://onlinelibrary.wiley.com/doi/pdf/10.1049/joe.2018.8316
Reference25 articles.
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3. LSTM: a search space odyssey;Greff K.;IEEE Trans. Neural Netw. Learn. Syst.,2015
4. AlahiA. GoelK. RamanathanV.et al.: ‘Social LSTM: human trajectory prediction in crowded spaces’.Proc. of the IEEE Conf. on Computer Vision and Pattern Recognition Las Vegas USA June 2016 pp.961–971
5. KalchbrennerN. DanihelkaI. GravesA.: ‘Grid long short‐term memory’ arXiv preprint arXiv: 1507.01526 2015
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