Multi-person Pose Tracking using Sequential Monte Carlo with Probabilistic Neural Pose Predictor
Masashi Okada,Shinji Takenaka,Tadahiro Taniguchi,Masashi Okada,Shinji Takenaka,Tadahiro Taniguchi
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 t...
