Visual detection and tracking algorithms for human motion-Reference-Cited by-同舟云学术

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Visual detection and tracking algorithms for human motion

Published:2023-05-04 Issue:30 Volume:82 Page:47165-47188
ISSN:1380-7501
Container-title:Multimedia Tools and Applications
language:en
Short-container-title:Multimed Tools Appl

Author:

Yang Ge^ORCID,Chen Siping

Funder

Major Scientific Research Project for Universities of Guangdong Province

Key Laboratory of Intelligent Multimedia Technology

Science and Technology Projects of Zhuhai in the Field of Social Development

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/s11042-023-15231-1.pdf

Reference20 articles.

1. Bilal M, Hanif MS (2020) Benchmark revision for HOG-SVM pedestrian detector through reinvigorated training and evaluation methodologies. IEEE Trans Intell Transp Syst 21(3):1277–1287

2. Chen K, Song X, Zhai X, Zhang B, Hou B, Wang Yi (2019) An integrated deep learning framework for occluded pedestrian tracking[J]. IEEE Access 7:26060–26072

3. Chu Y, Fei J, Hou S (2020) Adaptive global sliding-mode control for dynamic systems using double hidden layer recurrent neural network structure[J]. IEEE Transactions on Neural Networks and Learning Systems 31(4):1297–1309

4. Hasan I, Setti F, Tsesmelis T, Del Bue A, Galasso F, Cristani M (2018) MX-LSTM: mixing tracklets and vislets to jointly forecast trajectories and head poses[C]. IEEE/CVF conference on computer vision and pattern recognition, pp 6067–6076

5. Hasan I, Setti F, Tsesmelis T, Belagiannis V, Amin S, Del Bue A, Cristani M, Galasso F (2021) Forecasting people trajectories and head poses by jointly reasoning on tracklets and vislets[J]. IEEE Trans Pattern Anal Mach Intell 43(4):1267–1278

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