DukeMTMC4ReID

DukeMTMC4ReID dataset is a large-scale real-world person re-identification dataset based on DukeMTMC dataset. The authors use a fast state-of-the-art person detector algorithm, Doppia, for detecting people in video frames. After verified by the ground truth, for each identity, they uniformly sample 5 "good" bounding boxes in each available camera, while retaining all the "FP" bounding boxes in the corresponding frames. Some statistics about the dataset are provided below:

• images corresponding to 1,852 people existing across all the 8 cameras
• 1,413 unique identities with 22,515 bounding boxes that appear in more than one camera (valid identities)
• 439 distractor identities with 2,195 bounding boxes that appear in only one camera, in addition to 21,551 "FP" bounding boxes from the person detector
• the size of the bounding box varies from 72×34 pixels to 415×188 pixels

Related datasets

• DukeMTMC
• DukeMTMC-VideoReID
• DukeMTMC-reID
• DukeMTMC-attribute

Related publications:

• Mengran Gou, Srikrishna Karanam, Wenqian Liu, Octavia Camps, Richard J. Radke, "DukeMTMC4ReID: A Large-Scale Multi-Camera Person Re-Identification Dataset", IEEE Conference on Computer Vision and Pattern Recognition (CVPR), 2017.
• Ergys Ristani, Francesco Solera, Roger S. Zou, Rita Cucchiara, Carlo Tomasi, "Performance Measures and a Data Set for Multi-Target, Multi-Camera Tracking", European Conference on Computer Vision (ECCV) workshop on Benchmarking Multi-Target Tracking, 2016.