iLIDS-VID
Introduced by Li et al. in Unsupervised Person Re-identification by Deep Learning Tracklet Association
The iLIDS-VID dataset is a person re-identification dataset which involves 300 different pedestrians observed across two disjoint camera views in public open space. It comprises 600 image sequences of 300 distinct individuals, with one pair of image sequences from two camera views for each person. Each image sequence has variable length ranging from 23 to 192 image frames, with an average number of 73. The iLIDS-VID dataset is very challenging due to clothing similarities among people, lighting and viewpoint variations across camera views, cluttered background and random occlusions.
Source: http://www.eecs.qmul.ac.uk/~xiatian/downloads_qmul_iLIDS-VID_ReID_dataset.htmlBenchmarks
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iLIDS-VID
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GAF-Net
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iLIDS-VID
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uPMnet
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Papers
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