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Video-based human action recognition benefits from multiple cameras which can provide temporally synchronized, multi-view videos. Cross-video person identification, i.e., determining whether at a given time, persons tracked in different videos are the same person or not, is a key step to integrate such multi-view information for collaborative action recognition. For fixed cameras, this step is relatively easy since different cameras can be precalibrated. In this paper, we study cross-videodoi:10.1109/cvprw.2016.106 dblp:conf/cvpr/ZhengGFYW16 fatcat:ozhr3h75gvgorb7rwrrxwpqmae