  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 Monte Carlo (SMC). This naturally
reformulates the tracking scheme to handle multiple predictions (or hypotheses)
of poses, thereby mitigating the negative effect of prediction errors. An
important component of SMC, i.e., a proposal distribution, is designed as a
probabilistic neural pose predictor, which can propose diverse and plausible
hypotheses by incorporating epistemic uncertainty and heteroscedastic aleatoric
uncertainty. In addition, a recurrent architecture is introduced to our neural
modeling to utilize time-sequence information of poses to manage difficult
situations, such as the frequent disappearance and reappearances of poses.
Compared to existing baselines, the proposed method achieves a state-of-the-art
MOTA score on the PoseTrack2018 validation dataset by reducing approximately
50% of tracking errors from a state-of-the art baseline method.
