Re-identification methods often require well aligned, unoccluded detections of an entire subject. Such assumptions are impractical in real world scenarios, where people tend to form groups. To circumvent poor detection performance caused by occlusions, we use fixed regions of interest and employ codebook-based visual representations. We account for illumination variations between cameras using a coupled clustering method that learns per-camera codebooks with entries that correspond across cameras. Because predictable movement patterns exist in many scenarios, we also incorporate temporal context to improve re-identification performance. This includes learning expected travel times directly from data and using mutual exclusion constraints to encourage solutions that maintain temporal ordering. Our experiments illustrate the merits of the proposed approach in challenging re-identification scenarios including crowded public spaces.
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