Tracking People by Predicting 3D Appearance, Location and Pose
Author:
Affiliation:
1. UC Berkeley
Publisher
IEEE
Link
http://xplorestaging.ieee.org/ielx7/9878378/9878366/09880436.pdf?arnumber=9880436
Reference47 articles.
1. GNN3DMOT: Graph Neural Network for 3D Multi-Object Tracking With 2D-3D Multi-Feature Learning
2. Learning to Track with Object Permanence
3. A Simplex Method for Function Minimization
4. TrackFormer: Multi-Object Tracking with Transformers
5. Single-Shot Multi-person 3D Pose Estimation from Monocular RGB
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1. Adaptive Multi-person Tracking Method considering Appearance Changes;JOURNAL OF BROADCAST ENGINEERING;2023-11-30
2. Snipper: A Spatiotemporal Transformer for Simultaneous Multi-Person 3D Pose Estimation Tracking and Forecasting on a Video Snippet;IEEE Transactions on Circuits and Systems for Video Technology;2023-09
3. Person 3D Tracking and Action Recognition through Stereo Camera;2023 14th IIAI International Congress on Advanced Applied Informatics (IIAI-AAI);2023-07-08
4. On the Benefits of 3D Pose and Tracking for Human Action Recognition;2023 IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR);2023-06
5. Visibility Aware Human-Object Interaction Tracking from Single RGB Camera;2023 IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR);2023-06
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