Tracking a walking person using activity-guided annealed particle filtering

Tracking human pose using observations from less than three cameras is a challenging task due to ambiguity in the available image evidence. This work presents a method for tracking using a pre-trained model of activity to guide sampling within an Annealed Particle Filtering framework. The approach is an example of model-based analysis-bysynthesis and is capable of robust tracking from less than 3 cameras with reduced numbers of samples. We test the scheme on a common dataset containing ground truth motion capture data and compare against quantitative results for standard Annealed Particle Filtering. We find lower absolute and relative error scores for both monocular and 2camera sequences using 80% fewer particles.

• Type: Conference
• Year: 2008
• Where: FGR
• Authors: John Darby, Baihua Li, Nicholas Costen
• Keywords: Annealed Particle, Biometrics, FGR 2008, Particle Filtering, Particle Filtering Framework