A novel egocentric dataset collected from social mobile manipulator JackRabbot. The dataset includes 64 minutes of annotated multimodal sensor data including stereo cylindrical 360 degrees RGB video at 15 fps, 3D point clouds from two Velodyne 16 Lidars, line 3D point clouds from two Sick Lidars, audio signal, RGB-D video at 30 fps, 360 degrees spherical image from a fisheye camera and encoder values from the robot's wheels. Source: JRDB: A Dataset and Benchmark of Egocentric Visual Perception for Navigation in Human Environments

UCF-CC-50 is a dataset for crowd counting and consists of images of extremely dense crowds. It has 50 images with 63,974 head center annotations in total. The head counts range between 94 and 4,543 per image. The small dataset size and large variance make this a very challenging counting dataset. Source: Active Crowd Counting with Limited Supervision

TinyPerson is a benchmark for tiny object detection in a long distance and with massive backgrounds. The images in TinyPerson are collected from the Internet. First, videos with a high resolution are collected from different websites. Second, images from the video are sampled every 50 frames. Then images with a certain repetition (homogeneity) are deleted, and the resulting images are annotated with 72,651 objects with bounding boxes by hand. Source: https://arxiv.org/abs/1912.10664 Image Source: https://arxiv.org/pdf/1912.10664.pdf

MIAP is a dataset created by obtaining a new set of annotations on a subset of the Open Images dataset, containing bounding boxes and attributes for all of the people visible in those images, as the original Open Images dataset annotations are not exhaustive, with bounding boxes and attribute labels for only a subset of the classes in each image. The MIAP dataset focuses on enabling ML Fairness research. It provides additional annotations for 100,000 (70k from training and 30k from validation/test) images that contain at least one person bounding box in the original annotations. These additional annotations provide exhaustive bounding boxes for all people in an image. Person boxes are further annotated with attribute labels for fairness research. Annotated attributes include the human perceived gender presentation (predominantly feminine, predominantly masculine, and unknown) and perceived age range (young, middle, older, and unknown) of the localized person. This procedure adds nearly 100,000 new boxes that were not annotated under the original labeling pipeline. Annotations on the exhaustive set enable research into the fairness properties of models trained on partial annotations and the pipelines that produce these annotations.

PANDA is the first gigaPixel-level humAN-centric viDeo dAtaset, for large-scale, long-term, and multi-object visual analysis. The videos in PANDA were captured by a gigapixel camera and cover real-world scenes with both wide field-of-view (~1 square kilometer area) and high-resolution details (~gigapixel-level/frame). The scenes may contain 4k head counts with over 100x scale variation. PANDA provides enriched and hierarchical ground-truth annotations, including 15,974.6k bounding boxes, 111.8k fine-grained attribute labels, 12.7k trajectories, 2.2k groups and 2.9k interactions. Source: PANDA: A Gigapixel-level Human-centric Video Dataset

PTB-TIR is a Thermal InfraRed (TIR) pedestrian tracking benchmark, which provides 60 TIR sequences with mannuly annoations. The benchmark is used to fair evaluate TIR trackers. Source: PTB-TIR: A Thermal Infrared Pedestrian Tracking Benchmark

We learn high fidelity human depths by leveraging a collection of social media dance videos scraped from the TikTok mobile social networking application. It is by far one of the most popular video sharing applications across generations, which include short videos (10-15 seconds) of diverse dance challenges as shown above. We manually find more than 300 dance videos that capture a single person performing dance moves from TikTok dance challenge compilations for each month, variety, type of dances, which are moderate movements that do not generate excessive motion blur. For each video, we extract RGB images at 30 frame per second, resulting in more than 100K images. We segmented these images using Removebg application, and computed the UV coordinates from DensePose. Download TikTok Dataset: * Please use the dataset only for the research purpose. * The dataset can be viewed and downloaded from the Kaggle page. (you need to make an account in Kaggle to be able to download the data. It is free!) * The dataset can also be downloaded from here (42 GB). The dataset resolution is: (1080 x 604) * The original YouTube videos corresponding to each sequence and the dance name can be downloaded from here (2.6 GB).

In recent years, person detection and human pose estimation have made great strides, helped by large-scale labeled datasets. However, these datasets had no guarantees or analysis of human activities, poses, or context diversity. Additionally, privacy, legal, safety, and ethical concerns may limit the ability to collect more human data. An emerging alternative to real-world data that alleviates some of these issues is synthetic data. However, creation of synthetic data generators is incredibly challenging and prevents researchers from exploring their usefulness. Therefore, we release a human-centric synthetic data generator PeopleSansPeople which contains simulation-ready 3D human assets, a parameterized lighting and camera system, and generates 2D and 3D bounding box, instance and semantic segmentation, and COCO pose labels. Using PeopleSansPeople, we performed benchmark synthetic data training using a Detectron2 Keypoint R-CNN variant [1]. We found that pre-training a network using synthetic data and fine-tuning on target real-world data (few-shot transfer to limited subsets of COCO-person train [2]) resulted in a keypoint AP of 60.37±0.48 (COCO test-dev2017) outperforming models trained with the same real data alone (keypoint AP of 55.80) and pre-trained with ImageNet (keypoint AP of 57.50). This freely-available data generator should enable a wide range of research into the emerging field of simulation to real transfer learning in the critical area of human-centric computer vision.

A large-scale video portrait dataset that contains 291 videos from 23 conference scenes with 14K frames. This dataset contains various teleconferencing scenes, various actions of the participants, interference of passers-by and illumination change.

The SoccerNet Game State Reconstruction task is a novel high level computer vision task that is specific to sports analytics. It aims at recognizing the state of a sport game, i.e., identifying and localizing all sports individuals (players, referees, ..) on the field based on a raw input videos. SoccerNet-GSR is composed of 200 video sequences of 30 seconds, annotated with 9.37 million line points for pitch localization and camera calibration, as well as over 2.36 million athlete positions on the pitch with their respective role, team, and jersey number.

C2A: Combination to Application Dataset ## Overview This repository contains the code and information for the paper "UAV-Enhanced Combination to Application: Comprehensive Analysis and Benchmarking of a Human Detection Dataset for Disaster Scenarios" by Ragib Amin Nihal, Benjamin Yen, Katsutoshi Itoyama, and Kazuhiro Nakadai. The C2A (Combination to Application) dataset is a novel synthetic dataset designed to advance human detection in disaster scenarios using UAV imagery. It combines disaster scene backgrounds from the AIDER dataset with human poses from the LSP/MPII-MPHB dataset to create a comprehensive resource for training machine learning models. The full paper is available at: https://arxiv.org/pdf/2408.04922 ## Dataset The C2A dataset consists of 10,215 images containing over 360,000 annotated human instances in various disaster scenarios. It includes diverse human poses (bent, kneeling, lying, sitting, upright) and disaster contexts (traffic incidents, fire, flood, collapsed buildings). ### Usage Notes - The dataset is split into training, validation, and test sets. - Two annotation formats are provided for flexibility: YOLO and COCO. - Pose information is available in a separate folder for all images. - Users can choose between standard object detection (YOLO/COCO) or pose-aware detection (All labels with Pose info). ## Key Features - Synthetic dataset combining real disaster scenes with human poses - Over 360,000 annotated human instances - 5 human pose categories - 4 disaster scenario types - Image resolutions ranging from 123x152 to 5184x3456 pixels - Designed to improve human detection in complex disaster environments

A dataset to encourage research in these environments. It consists of labeled stereo video of people in orange and apple orchards taken from two perception platforms (a tractor and a pickup truck), along with vehicle position data from RTK GPS. Source: Comparing Apples and Oranges: Off-Road Pedestrian Detection on the NREC Agricultural Person-Detection Dataset

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

This dataset consists of 600+ items of faces with different emotions and mixed races that are ready to use for optimizing the accuracy of computer vision models. Human age range from 20 to 60 years old, balance in gender, no occlusion, with head direction (<45 degree up-down and left-right). All of the contents is sourced from PIXTA's stock library of 100M+ Asian-featured images and videos. PIXTA is the largest platform of visual materials in the Asia Pacific region offering fully-managed services, high quality contents and data, and powerful tools for businesses & organizations to enable their creative and machine learning projects. For more details, please refer to the link: https://www.pixta.ai/ Or send your inquiries to contact@pixta.ai