Gait Recognition

Gait3D is a large-scale 3D representation-based gait recognition dataset. It contains 4,000 subjects and over 25,000 sequences extracted from 39 cameras in an unconstrained indoor scene.

CASIA-B is a large multiview gait database, which is created in January 2005. There are 124 subjects, and the gait data was captured from 11 views. Three variations, namely view angle, clothing and carrying condition changes, are separately considered. Besides the video files, we still provide human silhouettes extracted from video files. The detailed information about Dataset B and an evaluation framework can be found in this paper . The format of the video filename in Dataset B is 'xxx-mm-nn-ttt.avi', where xxx: subject id, from 001 to 124. mm: walking status, can be 'nm' (normal), 'cl' (in a coat) or 'bg' (with a bag). nn: sequence number. ttt: view angle, can be '000', '018', ..., '180'.

The OU-ISIR Gait Database, Multi-View Large Population Dataset (OU-MVLP) is meant to aid research efforts in the general area of developing, testing and evaluating algorithms for cross-view gait recognition. The Institute of Scientific and Industrial Research (ISIR), Osaka University (OU) has copyright in the collection of gait video and associated data and serves as a distributor of the OU-ISIR Gait Database. The data was collected in conjunction with an experience-based long-run exhibition of video-based gait analysis at a science museum. The approved informed consent was obtained from all the subjects in this dataset. The dataset consists of 10,307 subjects (5,114 males and 5,193 females with various ages, ranging from 2 to 87 years) from 14 view angles, ranging 0°-90°, 180°-270°. Gait images of 1,280 x 980 pixels at 25 fps are captured by seven network cameras (Cam1-7) placed at intervals of 15-deg azimuth angles along a quarter of a circle whose center coincides with the center of the walking course. Its radius is approximately 8 m and height is approximately 5 m.

The USF Human ID Gait Challenge Dataset is a dataset of videos for gait recognition. It has videos from 122 subjects in up to 32 possible combinations of variations in factors. Source: http://www.eng.usf.edu/cvprg/Gait_Data.html

TUM-GAID (TUM Gait from Audio, Image and Depth) collects 305 subjects performing two walking trajectories in an indoor environment. The first trajectory is traversed from left to right and the second one from right to left. Two recording sessions were performed, one in January, where subjects wore heavy jackets and mostly winter boots, and another one in April, where subjects wore lighter clothes. The action is captured by a Microsoft Kinect sensor which provides a video stream with a resolution of 640×480 pixels and a frame rate around 30 FPS. Source: Energy-based Tuning of Convolutional Neural Networks on Multi-GPUs Image Source: https://www.ei.tum.de/mmk/verschiedenes/tum-gaid-database/

Psychological trait estimation from external factors such as movement and appearance is a challenging and long-standing problem in psychology, and is principally based on the psychological theory of embodiment. To date, attempts to tackle this problem have utilized private small-scale datasets with intrusive body-attached sensors. Potential applications of an automated system for psychological trait estimation include estimation of occupational fatigue and psychology, and marketing and advertisement. In this work, we propose PsyMo (Psychological traits from Motion), a novel, multi-purpose and multi-modal dataset for exploring psychological cues manifested in walking patterns. We gathered walking sequences from 312 subjects in 7 different walking variations and 6 camera angles. In conjunction with walking sequences, participants filled in 6 psychological questionnaires, totalling 17 psychometric attributes related to personality, self-esteem, fatigue, aggressiveness and mental health. We propose two evaluation protocols for psychological trait estimation. Alongside the estimation of self-reported psychological traits from gait, the dataset can be used as a drop-in replacement to benchmark methods for gait recognition. We anonymize all cues related to the identity of the subjects and publicly release only silhouettes, 2D / 3D human skeletons and 3D SMPL human meshes.

CCGR (Cross-Covariate Gait Recognition), the first gait dataset for studying cross-covariate challenges, contains 970 subjects, approximately 1.6 million sequences, 53 covariates, and 33 views.

Gait3D-Parsing is a dataset for gait recognition in the wild. It is an extension of the large-scale and challenging Gait-3D dataset which is collected from an in-the-wild environment. The train set has 3,000 IDs, and the test set has 1,000 IDs. Meanwhile, 1,000 sequences in the test set are taken as the query set, and the rest of the test set is taken as the gallery set.

The OU-ISIR Gait Database, Multi-View Large Population Database with Pose Sequence (OUMVLP-Pose) is meant to aid research efforts in the general area of developing, testing and evaluating algorithms for model-based gait recognition. This data set was built upon OU-MVLP. It contains 10,307 subjects of round-trip walking sequences captured by seven network cameras at intervals of 15° (this sums to 14 views by considering the round trip on the same walking course) with an image size of 1,280 x 980 pixels and a frame-rate of 25 fps.