Public Transportation Datasets : Différence entre versions
De Transport
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* PEMS-BAY / METR-LA liyaguang/DCRNN: Implementation of Diffusion Convolutional Recurrent Neural Network in Tensorflow https://github.com/liyaguang/DCRNN | * PEMS-BAY / METR-LA liyaguang/DCRNN: Implementation of Diffusion Convolutional Recurrent Neural Network in Tensorflow https://github.com/liyaguang/DCRNN | ||
* NAVER-SEOUL, HyunWookL/PM-MemNet https://github.com/HyunWookL/PM-MemNet | * NAVER-SEOUL, HyunWookL/PM-MemNet https://github.com/HyunWookL/PM-MemNet | ||
| − | * TJRD TS: http://tjrdts.linknova.cn (vehicle were | + | * TJRD TS: http://tjrdts.linknova.cn (vehicle were tracked through millimeter wave radar sensors installed along the freeways in sequence, and trajectories were spliced) |
| + | * Trajnet++ pedestrian trajectory detection benchmark https://www.aicrowd.com/challenges/trajnet-a-trajectory-forecasting-challenge | ||
* pNEUMA https://open-traffic.epfl.ch/ | * pNEUMA https://open-traffic.epfl.ch/ | ||
* Uber movement data https://movement.uber.com/?lang=en-CA | * Uber movement data https://movement.uber.com/?lang=en-CA | ||
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==[[PolyDatasets|PolyIT Datasets]]== | ==[[PolyDatasets|PolyIT Datasets]]== | ||
Generally not public, although it would be nice. | Generally not public, although it would be nice. | ||
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| + | Public datasets should be put on Borealis https://borealisdata.ca | ||
==Automated Vehicles== | ==Automated Vehicles== | ||
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* Mobility Aids http://mobility-aids.informatik.uni-freiburg.de/ | * Mobility Aids http://mobility-aids.informatik.uni-freiburg.de/ | ||
* X world, EvalAI (CVPR2022 AVA Accessibility Vision and Autonomy Challenge) https://eval.ai/challenge/1690/overview https://openaccess.thecvf.com/content/ICCV2021/papers/Zhang_X-World_Accessibility_Vision_and_Autonomy_Meet_ICCV_2021_paper.pdf | * X world, EvalAI (CVPR2022 AVA Accessibility Vision and Autonomy Challenge) https://eval.ai/challenge/1690/overview https://openaccess.thecvf.com/content/ICCV2021/papers/Zhang_X-World_Accessibility_Vision_and_Autonomy_Meet_ICCV_2021_paper.pdf | ||
| + | * UCLA activity dataset https://vcla.stat.ucla.edu/Projects/Multiscale_Activity_Recognition/ | ||
| + | * Multi-Object Multi-Actor, The first benchmark and dataset dedicated to activity parsing https://moma.stanford.edu | ||
==LIDAR datasets== | ==LIDAR datasets== | ||
Version du 22 février 2023 à 17:06
Sommaire
Traffic Data
- PEMS-BAY / METR-LA liyaguang/DCRNN: Implementation of Diffusion Convolutional Recurrent Neural Network in Tensorflow https://github.com/liyaguang/DCRNN
- NAVER-SEOUL, HyunWookL/PM-MemNet https://github.com/HyunWookL/PM-MemNet
- TJRD TS: http://tjrdts.linknova.cn (vehicle were tracked through millimeter wave radar sensors installed along the freeways in sequence, and trajectories were spliced)
- Trajnet++ pedestrian trajectory detection benchmark https://www.aicrowd.com/challenges/trajnet-a-trajectory-forecasting-challenge
- pNEUMA https://open-traffic.epfl.ch/
- Uber movement data https://movement.uber.com/?lang=en-CA
- highD dataset: new dataset of naturalistic vehicle trajectories recorded on German highways, using a drone https://www.highd-dataset.com/
- Open Data portals
- Detector-based traffic data from many countries https://utd19.ethz.ch/index.html
- TRB Traffic flow theory and characteristics committee (AHB45) http://tft.eng.usf.edu/docs.htm (bottom of the page)
- includes links to Portland ITS Portal, PeMS in California, etc.
- NGSIM dataset: traffic data for highways and urban corridors taken from multiple cameras on high buildings https://catalog.data.gov/dataset/next-generation-simulation-ngsim-vehicle-trajectories
- Mobile century data https://bayen.berkeley.edu/downloads/mobile-century-data
- Traffic control data from the US
PolyIT Datasets
Generally not public, although it would be nice.
Public datasets should be put on Borealis https://borealisdata.ca
Automated Vehicles
- Argoverse https://www.argoverse.org/
- Waymo open https://waymo.com/open
- Nuscenes https://www.nuscenes.org
- Volvo https://developer.volvocars.com/open-datasets/cirrus/
Image datasets of known objects are useful to train and test object classifiers
- Public video data set for road transportation applications (PDTV) http://www.tft.lth.se/video/co-operation/data-exchange/ ftp://barbapappa.tft.lth.se/
- Old synthetic and traffic video data http://i21www.ira.uka.de/image_sequences/
- Comprehensive cars dataset: http://mmlab.ie.cuhk.edu.hk/datasets/comp_cars/index.html
- MIT car data http://cbcl.mit.edu/software-datasets/CarData.html and person data http://cbcl.mit.edu/software-datasets/PedestrianData.html
- MIT Traffic Dataset http://www.ee.cuhk.edu.hk/~xgwang/MITtraffic.html
- UIUC car detection http://cogcomp.cs.illinois.edu/Data/Car/ and CMU car data http://vasc.ri.cmu.edu/idb/html/car/
- UCSD method for people counting with dataset http://www.svcl.ucsd.edu/projects/peoplecnt/
- Oxford annotated pedestrian dataset (Town Centre) http://www.robots.ox.ac.uk/ActiveVision/Research/Projects/2009bbenfold_headpose/project.html#datasets
- PETS datasets http://www.cvg.rdg.ac.uk/slides/pets.html
- 2009: people tracking with multiple cameras http://www.cvg.rdg.ac.uk/PETS2009/ (http://www.cvg.rdg.ac.uk/PETS2009/a.html)
- 2001: people and cars ftp://ftp.pets.rdg.ac.uk/pub/PETS2001/
- CityCars and CityPedestrians http://www.psi.toronto.edu/index.php?q=flobject%20analysis
- Gavrila http://www.gavrila.net/Research/Pedestrian_Detection/Daimler_Pedestrian_Benchmark_D/daimler_pedestrian_benchmark_d.html
- INRIA dataset used by N. Dalal (HoG classifiers) http://pascal.inrialpes.fr/data/human/
- Multi-View Car Dataset EPFL http://cvlab.epfl.ch/data/pose/
- Multiple object type (including cars) from multiple view http://www.vision.caltech.edu/savarese/3Ddataset.html
- Pascal-type object datasets: http://www.image-net.org/challenges/LSVRC/2012/browse-synsets
- ETH datasets http://www.vision.ee.ethz.ch/datasets/index.en.html
- VIRAT Video Dataset (surveillance, road users, car parks) http://www.viratdata.org/
- NGSIM dataset: highways and urban corridors taken from multiple cameras on high buildings, with the computed results http://ngsim-community.org/
- The PASCAL Visual Object Classes Homepage contains sets of images of objects of various types, including people, bicycles, cars, etc. http://pascallin.ecs.soton.ac.uk/challenges/VOC/ (see also MIT SUN dataset http://groups.csail.mit.edu/vision/SUN/ and Caltech http://www.vision.caltech.edu/Image_Datasets/Caltech256/, MIT CBCL StreetScenes http://cbcl.mit.edu/software-datasets/streetscenes/)
- KITTI vision benchmark suite (images+lidar) http://www.cvlibs.net/datasets/kitti/ (object detection benchmark http://www.cvlibs.net/datasets/kitti/eval_object.php) and Karlsruhe objects http://www.cvlibs.net/datasets/karlsruhe_objects.html
- Longterm Observation of Scenes with Tracks Dataset (LOST) at WUSL http://lost.cse.wustl.edu/browse
- TRaffic ANd COngestionS (TRANCOS) dataset, a novel benchmark for (extremely overlapping) vehicle counting in traffic congestion situation http://agamenon.tsc.uah.es/Personales/rlopez/data/trancos/
- GRAM Road-Traffic Monitoring (GRAM-RTM) dataset, a novel benchmark for multi-vehicle tracking in real-time http://agamenon.tsc.uah.es/Personales/rlopez/data/rtm/
- Amazing online open source tool for annotation (and using Amazon mechanical turk) http://mit.edu/vondrick/vatic/
- The Comprehensive Cars (CompCars) dataset http://mmlab.ie.cuhk.edu.hk/datasets/comp_cars/index.html
- Cityscapes Dataset (fine and coarse segmentation) https://www.cityscapes-dataset.com/
- with CityPersons https://arxiv.org/abs/1702.05693
- Traffic sign detection challenge http://benchmark.ini.rub.de/?section=gtsdb&subsection=news
- Common objects in context (Microsoft COCO dataset) http://cocodataset.org
- Miovision Traffic Camera Dataset http://podoce.dinf.usherbrooke.ca/
- Synthia dataset (SYNTHetic collection of Imagery and Annotations) http://synthia-dataset.net/
- WIDER Face & Pedestrian Challenge - Track 2: Pedestrian Detection https://competitions.codalab.org/competitions/19118
- BDD100K: A Large-scale Diverse Driving Video Database: http://bair.berkeley.edu/blog/2018/05/30/bdd/
- Stanford Drone Dataset: http://cvgl.stanford.edu/projects/uav_data/
- The Unmanned Aerial Vehicle Benchmark: Object Detection and Tracking https://sites.google.com/site/daviddo0323/projects/uavdt
- Collective Activity Dataset: https://vhosts.eecs.umich.edu/vision//activity-dataset.html
- Abnormal Event Detection at 150 FPS: http://www.cse.cuhk.edu.hk/leojia/projects/detectabnormal/index.html
- Traffic Research, GRAPH@FIT, Brno University of Technology (camera calibration, car image box, speed measurements): https://medusa.fit.vutbr.cz/traffic/
- Vision Meets Drones http://aiskyeye.com/
- MOTChallenge: The Multiple Object Tracking Benchmark https://motchallenge.net/
- AI city challenge: https://www.aicitychallenge.org/ (dataset CityFlow)
- STREETS: A Novel Camera Network Dataset for Traffic Flow https://github.com/corey-snyder/STREETS
- MOT challenge, includes other datasets https://motchallenge.net/
- Event cameras
- MVSEC: The Multi Vehicle Stereo Event Camera Dataset https://daniilidis-group.github.io/mvsec/
- DSEC Dataset: A Stereo Event Camera Dataset for Driving Scenarios https://dsec.ifi.uzh.ch/
- DrivingStereo: A Large-Scale Dataset for Stereo Matching in Autonomous Driving Scenarios https://drivingstereo-dataset.github.io/
- Sydney group: driving around Sydney campus http://its.acfr.usyd.edu.au/datasets/
- Infrared data: FLIR https://www.flir.quebec/oem/adas/adas-dataset-form/
- Infrared and visual comparison: CAMEL https://camel.ece.gatech.edu/
- MOTSynth (pedestrian videos from GTA V): https://aimagelab.ing.unimore.it/imagelab/page.asp?IdPage=42
- Mobility Aids http://mobility-aids.informatik.uni-freiburg.de/
- X world, EvalAI (CVPR2022 AVA Accessibility Vision and Autonomy Challenge) https://eval.ai/challenge/1690/overview https://openaccess.thecvf.com/content/ICCV2021/papers/Zhang_X-World_Accessibility_Vision_and_Autonomy_Meet_ICCV_2021_paper.pdf
- UCLA activity dataset https://vcla.stat.ucla.edu/Projects/Multiscale_Activity_Recognition/
- Multi-Object Multi-Actor, The first benchmark and dataset dedicated to activity parsing https://moma.stanford.edu
LIDAR datasets
Interesting applications https://scholar.google.com/scholar?&q=lidar%20urban%20environment%20parking
- MulRan: Multimodal Range Dataset for Urban Place Recognition https://sites.google.com/view/mulran-pr
- Complex Urban LiDAR Data Set (more robotics?) http://irap.kaist.ac.kr/dataset
Driver Simulator/Naturalistic Driving
- Driver behavior profiling: An investigation with different smartphone sensors and machine learning https://github.com/jair-jr/driverBehaviorDataset
- Real World Driving to Assess Driver Workload https://www.hcilab.org/research/hcilab-driving-dataset/
- SIMULATOR STUDY I: A Multimodal Dataset for Various Forms of Distracted Driving https://osf.io/c42cn/
