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2D Articulated Pose Tracking Using Particle Filter with Partitioned Sampling and Model Constraints


In this paper, we develop a two-dimensional articulated body tracking algorithm based on the particle filtering method using partitioned sampling and model constraints. Particle filtering has been proven to be an effective approach in the object tracking field, especially when dealing with single-object tracking. However, when applying it to human body tracking, we have to face a “particle-explosion” problem. We then introduce partitioned sampling, applied to a new articulated human body model, to solve this problem. Furthermore, we develop a propagating method originated from belief propagation (BP), which enables a set of particles to carry several constraints. The proposed algorithm is then applied to tracking articulated body motion in several testing scenarios. The experimental results indicate that the proposed algorithm is effective and reliable for 2D articulated pose tracking.

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Correspondence to Chenguang Liu.

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This work is supported by the National Natural Science Foundation of China (No.60672090) and partly supported by the Municipal Special Foundation of Harbin for Scientific and Technological Innovation Research (No.2006RFXXG013).

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Liu, C., Liu, P., Liu, J. et al. 2D Articulated Pose Tracking Using Particle Filter with Partitioned Sampling and Model Constraints. J Intell Robot Syst 58, 109–124 (2010).

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