Inter-camera Association of Multi-target Tracks by On-Line Learned Appearance Affinity Models


We propose a novel system for associating multi-target tracks across multiple non-overlapping cameras by an on-line learned discriminative appearance affinity model. Collecting reliable training samples is a major challenge in on-line learning since supervised correspondence is not available at runtime. To alleviate the inevitable ambiguities in these… (More)
DOI: 10.1007/978-3-642-15549-9_28


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