Learning articulated body models for people re-identification
Proceedings of the 21st ACM international conference on Multimedia
People reidentification in surveillance and forensics: A survey
ACM Computing Surveys (CSUR)
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Recent advances have shown that clothing appearance provides important features for person re-identification and retrieval in surveillance and multimedia data. However, the regions from which such features are extracted are usually only very crudely segmented, due to the difficulty of segmenting highly articulated entities such as persons. In order to overcome the problem of unconstrained poses, we propose a segmentation approach based on a large number of part detectors. Our approach is able to separately segment a person's upper and lower clothing regions, taking into account the person's body pose. We evaluate our approach on the task of character retrieval on a new challenging data set and present promising results.