Person Re-Identification

Market-1501 is a large-scale public benchmark dataset for person re-identification. It contains 1501 identities which are captured by six different cameras, and 32,668 pedestrian image bounding-boxes obtained using the Deformable Part Models pedestrian detector. Each person has 3.6 images on average at each viewpoint. The dataset is split into two parts: 750 identities are utilized for training and the remaining 751 identities are used for testing. In the official testing protocol 3,368 query images are selected as probe set to find the correct match across 19,732 reference gallery images. Source: A Survey of Pruning Methods for Efficient Person Re-identification Across Domains Image Source: https://www.researchgate.net/publication/306358716_A_Discriminative_Null_Space_based_Deep_Learning_Approach_for_Person_Re-Identification

The CUHK03 consists of 14,097 images of 1,467 different identities, where 6 campus cameras were deployed for image collection and each identity is captured by 2 campus cameras. This dataset provides two types of annotations, one by manually labelled bounding boxes and the other by bounding boxes produced by an automatic detector. The dataset also provides 20 random train/test splits in which 100 identities are selected for testing and the rest for training Source: Attention Driven Person Re-identification Image Source: Person Re-Identification Techniques for Intelligent Video Surveillance Systems

The DukeMTMC-reID (Duke Multi-Tracking Multi-Camera ReIDentification) dataset is a subset of the DukeMTMC for image-based person re-ID. The dataset is created from high-resolution videos from 8 different cameras. It is one of the largest pedestrian image datasets wherein images are cropped by hand-drawn bounding boxes. The dataset consists 16,522 training images of 702 identities, 2,228 query images of the other 702 identities and 17,661 gallery images. NOTE: This dataset has been retracted. Source: Deep Co-attention based Comparators For Relative Representation Learning in Person Re-identification

MSMT17 is a multi-scene multi-time person re-identification dataset. The dataset consists of 180 hours of videos, captured by 12 outdoor cameras, 3 indoor cameras, and during 12 time slots. The videos cover a long period of time and present complex lighting variations, and it contains a large number of annotated identities, i.e., 4,101 identities and 126,441 bounding boxes. The dataset can be requested at http://www.pkuvmc.com/dataset.html

MARS (Motion Analysis and Re-identification Set) is a large scale video based person reidentification dataset, an extension of the Market-1501 dataset. It has been collected from six near-synchronized cameras. It consists of 1,261 different pedestrians, who are captured by at least 2 cameras. The variations in poses, colors and illuminations of pedestrians, as well as the poor image quality, make it very difficult to yield high matching accuracy. Moreover, the dataset contains 3,248 distractors in order to make it more realistic. Deformable Part Model and GMMCP tracker were used to automatically generate the tracklets (mostly 25-50 frames long). Source: Multi-Target Tracking in Multiple Non-Overlapping Cameras using Constrained Dominant Sets

The Viewpoint Invariant Pedestrian Recognition (VIPeR) dataset includes 632 people and two outdoor cameras under different viewpoints and light conditions. Each person has one image per camera and each image has been scaled to be 128×48 pixels. It provides the pose angle of each person as 0° (front), 45°, 90° (right), 135°, and 180° (back). Source: PaMM: Pose-aware Multi-shot Matching for Improving Person Re-identification Image Source: Qin et al

The CUKL-SYSY dataset is a large scale benchmark for person search, containing 18,184 images and 8,432 identities. Different from previous re-id benchmarks, matching query persons with manually cropped pedestrians, this dataset is much closer to real application scenarios by searching person from whole images in the gallery. Source: http://www.ee.cuhk.edu.hk/~xgwang/PS/dataset.html Image Source: http://www.ee.cuhk.edu.hk/~xgwang/PS/dataset.html

The SYSU-MM01 is a dataset collected for the Visible-Infrared Re-identification problem. The images in the dataset were obtained from 491 different persons by recording them using 4 RGB and 2 infrared cameras. Within the dataset, the persons are divided into 3 fixed splits to create training, validation and test sets. In the training set, there are 20284 RGB and 9929 infrared images of 296 persons. The validation set contains 1974 RGB and 1980 infrared images of 99 persons. The testing set consists of the images of 96 persons where 3803 infrared images are used as query and 301 randomly selected RGB images are used as gallery. Source: An Efficient Framework for Visible-Infrared Cross Modality Person Re-Identification Image Source: https://github.com/wuancong/SYSU-MM01

PRW is a large-scale dataset for end-to-end pedestrian detection and person recognition in raw video frames. PRW is introduced to evaluate Person Re-identification in the Wild, using videos acquired through six synchronized cameras. It contains 932 identities and 11,816 frames in which pedestrians are annotated with their bounding box positions and identities.

RegDB is used for Visible-Infrared Re-ID which handles the cross-modality matching between the daytime visible and night-time infrared images. The dataset contains images of 412 people. It includes 10 color and 10 thermal images for each person. Source: Person Recognition System Based on a Combination of Body Images from Visible Light and Thermal Cameras

Occluded REID is an occluded person dataset captured by mobile cameras, consisting of 2,000 images of 200 occluded persons (see Fig. (c)). Each identity has 5 full-body person images and 5 occluded person images with different types of occlusion. Source: Foreground-aware Pyramid Reconstruction for Alignment-free Occluded Person Re-identification Image Source: https://github.com/tinajia2012/ICME2018_Occluded-Person-Reidentification_datasets

ETH is a dataset for pedestrian detection. The testing set contains 1,804 images in three video clips. The dataset is captured from a stereo rig mounted on car, with a resolution of 640 x 480 (bayered), and a framerate of 13--14 FPS. Source: Scale-aware Fast R-CNN for Pedestrian Detection Image Source: https://medium.com/@zhenqinghu/pedestrian-detection-on-eth-data-set-with-faster-r-cnn-19d0a906f1d3

A novel large-scale corpus of manual annotations for the SoccerNet video dataset, along with open challenges to encourage more research in soccer understanding and broadcast production. Source: SoccerNet-v2 : A Dataset and Benchmarks for Holistic Understanding of Broadcast Soccer Videos

The DukeMTMC-VideoReID (Duke Multi-Tracking Multi-Camera Video-based ReIDentification) dataset is a subset of the DukeMTMC for video-based person re-ID. The dataset is created from high-resolution videos from 8 different cameras. It is one of the largest pedestrian video datasets wherein images are cropped by hand-drawn bounding boxes. The dataset consists 4832 tracklets of 1812 identities in total, and each tracklet has 168 frames on average. NOTE: This dataset has been retracted. Source: Exploit the Unknown Gradually: One-Shot Video-Based Person Re-Identification by Stepwise Learning

CityFlow is a city-scale traffic camera dataset consisting of more than 3 hours of synchronized HD videos from 40 cameras across 10 intersections, with the longest distance between two simultaneous cameras being 2.5 km. The dataset contains more than 200K annotated bounding boxes covering a wide range of scenes, viewing angles, vehicle models, and urban traffic flow conditions. Camera geometry and calibration information are provided to aid spatio-temporal analysis. In addition, a subset of the benchmark is made available for the task of image-based vehicle re-identification (ReID). Source: CityFlow: A City-Scale Benchmark for Multi-Target Multi-Camera Vehicle Tracking and Re-Identification

UAV-Human is a large dataset for human behavior understanding with UAVs. It contains 67,428 multi-modal video sequences and 119 subjects for action recognition, 22,476 frames for pose estimation, 41,290 frames and 1,144 identities for person re-identification, and 22,263 frames for attribute recognition. The dataset was collected by a flying UAV in multiple urban and rural districts in both daytime and nighttime over three months, hence covering extensive diversities w.r.t subjects, backgrounds, illuminations, weathers, occlusions, camera motions, and UAV flying attitudes. This dataset can be used for UAV-based human behavior understanding, including action recognition, pose estimation, re-identification, and attribute recognition.

RSTPReid contains 20505 images of 4,101 persons from 15 cameras. Each person has 5 corresponding images taken by different cameras with complex both indoor and outdoor scene transformations and backgrounds in various periods of time, which makes RSTPReid much more challenging and more adaptable to real scenarios. Each image is annotated with 2 textual descriptions. For data division, 3701 (index < 18505), 200 (18505 <= index < 19505) and 200 (index >= 19505) identities are utilized for training, validation and testing, respectively (Marked by item 'split' in the JSON file). Each sentence is no shorter than 23 words.

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.

Partial REID is a specially designed partial person reidentification dataset that includes 600 images from 60 people, with 5 full-body images and 5 occluded images per person. These images were collected on a university campus by 6 cameras from different viewpoints, backgrounds and different types of occlusion. The examples of partial persons in the Partial REID dataset are shown in the Figure. Source: Foreground-aware Pyramid Reconstruction for Alignment-free Occluded Person Re-identification Image Source: https://arxiv.org/abs/1810.07399

This dataset consists of 33698 images from 221 identities. Each person in Cameras A and B is wearing the same clothes, but the images are captured in different rooms. For Camera C, the person wears different clothes, and the images are captured in a different day. Source: Dataset Homepage

Veri-Wild is the largest vehicle re-identification dataset (as of CVPR 2019). The dataset is captured from a large CCTV surveillance system consisting of 174 cameras across one month (30× 24h) under unconstrained scenarios. This dataset comprises 416,314 vehicle images of 40,671 identities. Evaluation on this dataset is split across three subsets: small, medium and large; comprising 3000, 5000 and 10,000 identities respectively (in probe and gallery sets). Source: Vehicle Re-Identification: an Efficient Baseline Using Triplet Embedding Image Source: https://github.com/PKU-IMRE/VERI-Wild

LTCC contains 17,119 person images of 152 identities, and each identity is captured by at least two cameras. The dataset can be divided into two subsets: one cloth-change set where 91 persons appear with 416 different sets of outfits in 14,783 images, and one cloth-consistent subset containing the remaining 61 identities with 2,336 images without outfit changes. On average, there are 5 different clothes for each cloth-changing person, with the number of outfit changes ranging from 2 to 14. Source: LTCC dataset homepage

Market-1501-C is an evaluation set that consists of algorithmically generated corruptions applied to the Market-1501 test-set. These corruptions consist of Noise: Gaussian, shot, impulse, and speckle; Blur: defocus, frosted glass, motion, zoom, and Gaussian; Weather: snow, frost, fog, brightness, spatter, and rain; Digital: contrast, elastic, pixel, JPEG compression, and saturate. Each corruption has five severity levels, resulting in 100 distinct corruptions.

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.html Image Source: http://www.eecs.qmul.ac.uk/~xiatian/downloads_qmul_iLIDS-VID_ReID_dataset.html

PRID 2011 is a person reidentification dataset that provides multiple person trajectories recorded from two different static surveillance cameras, monitoring crosswalks and sidewalks. The dataset shows a clean background, and the people in the dataset are rarely occluded. In the dataset, 200 people appear in both views. Among the 200 people, 178 people have more than 20 appearances Source: PaMM: Pose-aware Multi-shot Matching for Improving Person Re-identification

Person re-identification (Reid) is now an active research topic for AI-based video surveillance applications such as specific person search, but the practical issue that the target person(s) may change clothes (clothes inconsistency problem) has been overlooked for long. For the first time, this paper systematically studies this problem. We first overcome the difficulty of lack of suitable dataset, by collecting a small yet representative real dataset for testing whilst building a large realistic synthetic dataset for training and deeper studies. Facilitated by our new datasets, we are able to conduct various interesting new experiments for studying the influence of clothes inconsistency. We find that changing clothes makes Reid a much harder problem in the sense of bringing difficulties to learning effective representations and also challenges the generalization ability of previous Reid models to identify persons with unseen (new) clothes. Representative existing Reid models are adopted to show informative results on such a challenging setting, and we also provide some preliminary efforts on improving the robustness of existing models on handling the clothes inconsistency issue in the data. We believe that this study can be inspiring and helpful for encouraging more researches in this direction. The dataset is avaliable on the project website: https://wanfb.github.io/dataset.html.

A novel dataset facilitating multimodal and Synergetic sociAL Scene Analysis. Source: SALSA: A Novel Dataset for Multimodal Group Behavior Analysis

One large-scale database for Text-to-Image Person Re-identification, i.e., Text-based Person Retrieval. Compared with existing databases, ICFG-PEDES has three key advantages. First, its textual descriptions are identity-centric and fine-grained. Second, the images included in ICFG-PEDES are more challenging, containing more appearance variability due to the presence of complex backgrounds and variable illumination. Third, the scale of ICFG-PEDES is larger.

Clothes-Changing Video person re-ID (CCVID) is a dataset constructed from the raw data of a gait recognition dataset, i.e. FVG. The reconstructed CCVID dataset contains 347,833 bounding boxes. The length of each sequence changes from 27 to 410 frames, with an average length of 122. Besides, it also provides fine-grained clothes labels including tops, bottoms, shoes, carrying status, and accessories. For the convenience of evaluation, CCVID re-divides the training and test sets to adapt to clothes-changing re-id. Specifically, 75 identities are reserved for training, and the remaining 151 identities are used for test. In the test set, 834 sequences are used as query set, and the other 1074 sequences form gallery set.

SYSU-30k contains 30k categories of persons, which is about 20 times larger than CUHK03 (1.3k categories) and Market1501 (1.5k categories), and 30 times larger than ImageNet (1k categories). SYSU-30k contains 29,606,918 images. Moreover, SYSU-30k provides not only a large platform for the weakly supervised ReID problem but also a more challenging test set that is consistent with the realistic setting for standard evaluation. Source: https://github.com/wanggrun/SYSU-30k Image Source: https://github.com/wanggrun/SYSU-30k

Partial iLIDS is a dataset for occluded person person re-identification. It contains a total of 476 images of 119 people captured by 4 non-overlapping cameras. Some images contain people occluded by other individuals or luggage. Source: Foreground-aware Pyramid Reconstruction for Alignment-free Occluded Person Re-identification Image Source: https://arxiv.org/pdf/1904.04975.pdf

P-DukeMTMC-reID is a modified version based on DukeMTMC-reID dataset. There are 12,927 images (665 identifies) in training set, 2,163 images (634 identities) for querying and 9,053 images in the gallery set.

CUHK03-C is an evaluation set that consists of algorithmically generated corruptions applied to the CUHK03 test-set. These corruptions consist of Noise: Gaussian, shot, impulse, and speckle; Blur: defocus, frosted glass, motion, zoom, and Gaussian; Weather: snow, frost, fog, brightness, spatter, and rain; Digital: contrast, elastic, pixel, JPEG compression, and saturate. Each corruption has five severity levels, resulting in 100 distinct corruptions.

The Airport dataset is a dataset for person re-identification which consists of 39,902 images and 9,651 identities across six cameras. Source: An Evaluation of Deep CNN Baselines for Scene-Independent Person Re-Identification Image Source: http://www.northeastern.edu/alert/transitioning-technology/alert-datasets/alert-airport-re-identification-dataset/

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.

LReID is a benchmark for lifelong person reidentification. It has been built using existing datasets, and it consists of two subsets: LReID-Seen and LReID-Unseen. LReID-Seen contains 40,459 training images of the 2,500 identities selected from the following datasets: CUHK03, Market-1501, MSMT17 V2, DukeMTMC-ReID, CUHK-SYSU ReID. This is used to test a model's performance on seen domains. LReID-Unseen contains 9,854 images from 3,594 identities from the following datasets: VIPeR, PRID, GRID, i-LIDS, CUHK01, CUHK02, SenseReID. This subset is used to test the model's generalisation capabilities to unseen domains.