Multi-person Pose Tracking using Sequential Monte Carlo with Probabilistic Neural Pose Predictor

@article{Okada2020MultipersonPT,
  title={Multi-person Pose Tracking using Sequential Monte Carlo with Probabilistic Neural Pose Predictor},
  author={Masashi Okada and Shinji Takenaka and Tadahiro Taniguchi},
  journal={2020 IEEE International Conference on Robotics and Automation (ICRA)},
  year={2020},
  pages={10024-10030}
}
It is an effective strategy for the multi-person pose tracking task in videos to employ prediction and pose matching in a frame-by-frame manner. For this type of approach, uncertainty-aware modeling is essential because precise prediction is impossible. However, previous studies have relied on only a single prediction without incorporating uncertainty, which can cause critical tracking errors if the prediction is unreliable. This paper proposes an extension to this approach with Sequential… Expand
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