Articulated Pose Identification with Sparse Point Features

Bo Li, Qinggang Meng, Horst Holstein

Research output: Contribution to journalArticlepeer-review

17 Citations (SciVal)


We propose a general algorithm for identifying an arbitrary pose of an articulated subject with sparse point features. The algorithm aims to identify a one-to-one correspondence between a model point-set and an observed point-set taken from freeform motion of the articulated subject. We avoid common assumptions such as pose similarity or small motions with respect to the model, and assume no prior knowledge from which to infer an initial or partial correspondence between the two point-sets. The algorithm integrates local segment-based correspondences under a set of affine transformations, and a global hierarchical search strategy. Experimental results, based on synthetic pose and real-world human motion data demonstrate the ability of the algorithm to perform the identification task. Reliability is increasingly compromised with increasing data noise and segmental distortion, but the algorithm can tolerate moderate levels. This work contributes to establishing a crucial self-initializing identification in model-based point-feature tracking for articulated motion.
Original languageEnglish
Pages (from-to)1412-1422
Number of pages11
JournalIEEE Transactions on Systems, Man, and Cybernetics, Part B (Cybernetics)
Issue number3
Publication statusPublished - 01 Jun 2004


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