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Comparison of Multi-shot Models for Short-term Re-identification of People using RGB-D Sensors

This work explores different types of multi-shot descriptors for re-identification in an on-the-fly enrolled environment using RGB-D sensors. We present a full re-identification pipeline complete with detection, segmentation, feature extraction, and re-identification, which expands on previous work by using multi-shot descriptors modeling people over a full camera pass instead of single frames with no temporal linking. We compare two different multi-shot models; mean histogram and histogram series, and test them each in 3 different color spaces. Both histogram descriptors are assisted by a depth-based pruning step where unlikely candidates are filtered away. Tests are run on 3 sequences captured in different circumstances and lighting situations to ensure proper generalization and lighting/environment invariance.