Abstract
Reidentifying people across disjoint camera views, known as person reidentification (PRId), has gained attention in many surveillance applications. This letter presents a framework for PRId that jointly exploits orientation and appearance cues to filter a subset of gallery candidates, where the probability of finding an exact match for a given probe remains very high. In addition, a similarity measure is formulated using the bipartite graph matching together with the weights of feature channels to estimate the correspondence within the filtered set. Experimental evaluation on standard datasets alongside comparisons with state-of-the-art validates the efficacy of our approach.
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