Probabilistic Pedestrian Tracking Based on a Skeleton Model
Jumpei Ashida, Ryusuke Miyamoto, Hiroshi Tsutsui, Takao Onoye, Yukihiro Nakamura · 2006
A novel pedestrian tracking scheme based on a particle filter is proposed, which adopts a skeleton model of a pedestrian as a state space model and uses distance transformed images for likelihood estimation. The six-stick skeleton model used in the proposed approach is very distinctive in representing a pedestrian simply but effectively, with which the efficient state space for the pedestrian tracking can be derived. Experimental results by using PETS sample sequences demonstrate that the proposed approach achieves highly accurate pedestrian tracking without any of prior learning.