Video-Based Person Re-Identification

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 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 NOTE: This dataset has been retracted.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

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

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/

Provides consistent ID annotations across multiple days, making it suitable for the extremely challenging problem of person search, i.e., where no clothing information can be reliably used. Apart this feature, the P-DESTRE annotations enable the research on UAV-based pedestrian detection, tracking, re-identification and soft biometric solutions. Source: The P-DESTRE: A Fully Annotated Dataset for Pedestrian Detection, Tracking, Re-Identification and Search from Aerial Devices

The largest aerial-ground video dataset with 6,632 identities, 32,321 tracklets, and 9.6 million frames Multiple platforms combining aerial drones (at various altitudes: 15m, 30m, 80m, 120m), CCTV cameras, and wearable cameras Rich annotations including clothing changes and 15 soft-biometric attributes Real-world challenges including extreme viewpoint variations, scale differences, and occlusions Diverse environments with outdoor campus scenes captured over 20 days

MARS dataset processed with our re-Detect and Link (DL) module. More information: https://github.com/jackie840129/CF-AAN

The TVPReid dataset contains 6559 pedestrian videos, each of which is annotated with two text descriptions, for a total of 13118 descriptions. The sentence descriptions are in a natural language style and contain rich details about the pedestrian's appearance, actions, and environmental elements that the pedestrian interacts with. The average sentence length of the TVPReid dataset is 30 words, and the longest sentence contains 83 words.