Large-Scale Person Re-Identification

Occluded-DukeMTMC contains 15,618 training images, 17,661 gallery images, and 2,210 occluded query images. The experiment results on Occluded-DukeMTMC will demonstrate the superiority of our method in Occluded Person Re-ID problems, let alone that our method does not need any manually cropping procedure as pre-process.

Labeled Pedestrian in the Wild (LPW) is a pedestrian detection dataset that contains 2,731 pedestrians in three different scenes where each annotated identity is captured by from 2 to 4 cameras. The LPW features a notable scale of 7,694 tracklets with over 590,000 images as well as the cleanliness of its tracklets. It distinguishes from existing datasets in three aspects: large scale with cleanliness, automatically detected bounding boxes and far more crowded scenes with greater age span. This dataset provides a more realistic and challenging benchmark, which facilitates the further exploration of more powerful algorithms.

The growing importance of person re-identification in computer vision has highlighted the need for more extensive and diverse datasets. In response, we introduce the ENTIRe-ID dataset, an extensive collection comprising over 4.45 million images from 37 different cameras in varied environments. This dataset is uniquely designed to tackle the challenges of domain variability and model generalization, areas where existing datasets for person re-identification have fallen short. The ENTIRe-ID dataset stands out for its coverage of a wide array of real-world scenarios, encompassing various lighting conditions, angles of view, and diverse human activities. This design ensures a realistic and robust training platform for ReID models.