Multiple People Tracking

The AI City Challenge, hosted at CVPR 2024, focuses on harnessing AI to enhance operational efficiency in physical settings such as retail and warehouse environments, and Intelligent Traffic Systems (ITS). It aims to utilize AI for actionable insights from sensor data, like camera feeds, to improve traffic safety and transportation outcomes. This year, the challenge spotlights two key areas with significant potential: retail business and ITS.

Multi-camera Multiple People Tracking (MMPTRACK) dataset has about 9.6 hours of videos, with over half a million frame-wise annotations. The dataset is densely annotated, e.g., per-frame bounding boxes and person identities are available, as well as camera calibration parameters. Our dataset is recorded with 15 frames per second (FPS) in five diverse and challenging environment settings., e.g., retail, lobby, industry, cafe, and office. This is by far the largest publicly available multi-camera multiple people tracking dataset. We expect the availability of such large-scale multi-camera multiple people tracking dataset will encourage more participants in this research topic. This dataset is also valuable for the evaluation of other tasks, such as multi-view people detection and monocular multiple people tracking.

PersonPath22 is a large-scale multi-person tracking dataset containing 236 videos captured mostly from static-mounted cameras, collected from sources where we were given the rights to redistribute the content and participants have given explicit consent. Each video has ground-truth annotations including both bounding boxes and tracklet-ids for all the persons in each frame.

Dataset originally conceived for multi-face tracking/detection for highly crowded scenarios. In these scenarios, the face is the only part that can be used to track the individuals. All our videos present novel crowd scenes recorded at near-eye level, where faces are visible enough to be analysed at the microscopic level, while also benefiting from a macroscopic view of the crowd. It includes: - Face detections of 715 unique subjects along with instructions to download the synchronized video. - More than 75k face detections annotated. - A density ranging from 3 to 13 people/frame. - 6 indoor and 4 outdoor videos. 8/10 videos are totally unconstrained, 2/10 feature 3 re-appearances per subject. Our dataset may be useful for: - Face tracking, especially relevant for crowded scenarios (typically from video-surveillance cameras). - Heavily occluded body tracking (in many videos, only the face is mostly visible). - Face recognition. - Face detection for partially occluded faces.

SOMPT22 is a multi-object tracking (MOT) benchmark focused on surveillance-style pedestrian tracking. * 22 long video sequences (static pole-mounted cameras, 6 – 8 m height) * ~51 k annotated frames with bounding boxes + unique track IDs * Outdoor scenes with illumination changes, partial occlusions and appearance similarity * Single class: person * Split files ready for training/validation and standard MOT evaluation tools SOMPT22 aims to complement generic MOTChallenge-style datasets by stressing long-term ID maintenance under sparse-to-medium crowd density instead of dense, short clips. Homepage → https://sompt22.github.io Download → Google Drive link in the homepage Citation → ```bibtex @misc{simsek2022sompt22, author = {Simsek, Fatih Emre and Cigla, Cevahir and Kayabol, Koray}, title = {SOMPT22: A Surveillance Oriented Multi-Pedestrian Tracking Dataset}, year = {2022}, eprint = {2208.02580}, archivePrefix = {arXiv}, primaryClass = {cs.CV} }

This dataset is an extremely challenging set of over 3000+ original Crowd images captured and crowdsourced from over 300+ urban and rural areas, where each image is manually reviewed and verified by computer vision professionals at Datacluster Labs. ### Dataset Features - Dataset size : 3000+ - Captured by : Over 400+ crowdsource contributors - Resolution : 100% images HD and above (1920x1080 and above) - Location : Captured with 300+ cities accross India - Diversity : Various lighting conditions like day, night, varied distances, view points etc. - Device used : Captured using mobile phones in 2021-2022 - Usage : Human detection, CCTV monitoring of crowd, Crowd tracking, etc. ### Available Annotation formats COCO, YOLO, PASCAL-VOC, Tf-Record To download full datasets or to submit a request for your dataset needs, please ping us at sales@datacluster.ai Visit www.datacluster.ai to know more. Note: All the images are manually captured and verified by a large contributor base on DataCluster platform