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Pedestrian detection is the task of detecting pedestrians from a camera.
Further state-of-the-art results (e.g. on the KITTI dataset) can be found at 3D Object Detection.
<span style="color:grey; opacity: 0.6">( Image credit: High-level Semantic Feature Detection: A New Perspective for Pedestrian Detection )</span>
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The CityPersons dataset is a subset of Cityscapes which only consists of person annotations. There are 2975 images for training, 500 and 1575 images for validation and testing. The average of the number of pedestrians in an image is 7. The visible-region and full-body annotations are provided. Source: NMS by Representative Region: Towards Crowded Pedestrian Detection by Proposal Pairing Image Source: https://github.com/CharlesShang/Detectron-PYTORCH/tree/master/data/citypersons
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.
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
The INRIA Person dataset is a dataset of images of persons used for pedestrian detection. It consists of 614 person detections for training and 288 for testing. Source: http://pascal.inrialpes.fr/data/human/ Image Source: https://www.researchgate.net/figure/Some-human-examples-of-the-INRIA-person-dataset-4-Though-the-examples-are-aligned_fig2_224135181
A novel dataset for traffic accidents analysis. Source: CADP: A Novel Dataset for CCTV Traffic Camera based Accident Analysis
TJU-DHD is a high-resolution dataset for object detection and pedestrian detection. The dataset contains 115,354 high-resolution images (52% images have a resolution of 1624×1200 pixels and 48% images have a resolution of at least 2,560×1,440 pixels) and 709,330 labelled objects in total with a large variance in scale and appearance. Source: https://github.com/tjubiit/TJU-DHD Image Source: https://github.com/tjubiit/TJU-DHD
The EuroCity Persons dataset provides a large number of highly diverse, accurate and detailed annotations of pedestrians, cyclists and other riders in urban traffic scenes. The images for this dataset were collected on-board a moving vehicle in 31 cities of 12 European countries. With over 238,200 person instances manually labeled in over 47,300 images, EuroCity Persons is nearly one order of magnitude larger than person datasets used previously for benchmarking. The dataset furthermore contains a large number of person orientation annotations (over 211,200).
The Caltech Pedestrian Dataset consists of approximately 10 hours of 640x480 30Hz video taken from a vehicle driving through regular traffic in an urban environment. About 250,000 frames (in 137 approximately minute long segments) with a total of 350,000 bounding boxes and 2300 unique pedestrians were annotated. The annotation includes temporal correspondence between bounding boxes and detailed occlusion labels. Source: Caltech Pedestrian Dataset
The RailEye3D dataset, a collection of train-platform scenarios for applications targeting passenger safety and automation of train dispatching, consists of 10 image sequences captured at 6 railway stations in Austria. Annotations for multi-object tracking are provided in both an unified format as well as the ground-truth format used in the MOTChallenge.
BG Vulnerable Pedestrian (BGVP) is a dataset to help train well-rounded models and thus induce research to increase the efficacy of vulnerable pedestrian detection. The dataset contains 2,000 images with 5,932 bounding box instances from four categories, i.e., Children Without Disability, Elderly without Disability, With Disability, and Non-Vulnerable. Source: Comparison Of Deep Object Detectors On A New Vulnerable Pedestrian Dataset Image Source: https://github.com/devvansh1997/bgvp
UofTPed50 is an object detection and tracking dataset which uses GPS to ground truth the position and velocity of a pedestrian. It can be used for benchmarking the positional accuracy of 3D pedestrian detection. It contains accurate positioning information by attaching a GPS system to the pedestrian itself. This dataset consists of 50 sequences of varying distance, pedestrian trajectory, and ego-vehicle trajectory. Each sequence contains one pedestrian. The scenarios are broken into four groups.
InfraParis is a novel and versatile dataset supporting multiple tasks across three modalities: RGB, depth, and infrared. From the city to the suburbs, it contains a variety of styles in different areas of the greater Paris area, providing rich semantic information. InfraParis contains 7301 images with bounding boxes and full semantic (19 classes) annotations. We assess various state-of-the-art baseline techniques, encompassing models for the tasks of semantic segmentation, object detection, and depth estimation.
MMPD Dataset is proposed in ECCV'2024 "When Pedestrian Detection Meets Multi-Modal Learning: Generalist Model and Benchmark Dataset".
The National Institute of Informatics - Chiba University (NII-CU) Multispectral Aerial Person Detection Dataset consists of 5,880 pairs of aligned RGB+FIR (Far infrared) images captured from a drone flying at heights between 20 and 50 meters, with the cameras pointed at 45 degrees down. We applied lens distortion correction and a homography warping to align the thermal images with the RGB images. We then labeled the people visible on the images with rectangular bounding boxes. The footage shows a baseball field and surroundings in Chiba, Japan, recorded in January 2020. *RGB images captured with a Zenmuse X3 (3840x2160) *Thermal images captured with a FLIR Vue Pro 640 in White Hot palette (640x512 before alignment) *RGB images are corrected for lens distortion *Thermal images are corrected for lens distortion and warped to be aligned with RGB images *Labels are provided in CSV format
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
Virtual-PedCross-4667 is a dataset for pedestrian crossing prediction. It consists of 4667 video sequences, 2862 pedestrian crossing sequences and 1804 not-crossing sequences. Totally, 745k video frames with the resolution of 1280×720 are saved. Source: Deep Virtual-to-Real Distillation for Pedestrian Crossing Prediction Image Source: https://arxiv.org/pdf/2211.00856v1.pdf
We introduce the KAIST multi-spectral dataset, which covers a greater range of drivable regions, from urban to residential, for autonomous systems. Our dataset provides different perspectives of the world captured in coarse time slots (day and night) in addition to fine time slots (sunrise, morning, afternoon, sunset, night and dawn). For all-day perception of autonomous systems, we propose the use of a different spectral sensor, i.e., a thermal imaging camera. Toward this goal, we develop a multi-sensor platform, which supports the use of a co-aligned RGB/Thermal camera, RGB stereo, 3D LiDAR and inertial sensors (GPS/IMU) and a related calibration technique. We design a wide range of visual perception tasks including the object detection, drivable region detection, localization, image enhancement, depth estimation and colorization using a single/multi-spectral approach. In this paper, we provide a description of our benchmark with the recording platform, data format, development toolkits, and lessons about the progress of capturing datasets. Source: A Review on Deep Learning Techniques Applied to Semantic Segmentation Image Source: https://ieeexplore.ieee.org/document/8293689/figures#figures
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