Attributes for Re-identification
We present here our attributes ground-truth labels, as used in the below publications. Attributes are a compelling representation for re-identification; they're low-dimensional, often easy to classify, and lastly they are quite compatible with other standard tools such as distance-metric learning and transfer.
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- R. Layne, T. M. Hospedales, and S. Gong, “Person Re-identification by Attributes,” in British Machine Vision Conference, 2012.
- 15 attributes, 12 of which are appearance-based, the remaining 3 are soft-biometrics: (shorts, skirt, sandals, backpack, jeans, logo, v-neck, open-outerwear, stripes, sunglasses, headphones, long-hair, short-hair, gender, carrying- object).
- Labels provided for VIPeR, and PRID datasets (provided in .mat format)
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- R. Layne, T. M. Hospedales, and S. Gong, “Attributes-based Re-identification,” in Person Re-identification, S. Gong, M. Cristani, S. Yan, and C. C. Loy, Eds. Springer London, 2013, pp. 93–117.
- 21 attributes, in order of prevalence in VIPeR: (darkhair, hassatchel, darkbottoms, midhair, greenshirt, darkshirt, lightshirt, bald, jeans, lightpants, uniquepants, nocoat, redshirt, hasbaginhand, barelegs, skirt, ismale, hasbackpack, blueshirt, patterned, shorts).
- Labels provided for VIPeR, GRID, PRID datasets (provided in .mat format)
- Example script provided, illustrating best-case re-identification scores vs. standard L2 nearest-neighbour re-id.