crowdbotp · marcemq · Mar 31, 2022
diff --git a/README.md b/README.md
@@ -14,12 +14,12 @@ We introduce existing datasets for Human Trajectory Prediction (HTP) task, and a
 | ![](datasets/GC/reference.jpg) | [GC](datasets/GC) | Grand Central Train Station Dataset: 1 scene of 33:20 minutes of crowd trajectories <code>#Traj:[Peds=12,684]</code> <code>Coord=image-2D</code> <code>FPS=25</code> | [dropbox](https://www.dropbox.com/s/7y90xsxq0l0yv8d/cvpr2015_pedestrianWalkingPathDataset.rar) [paper](http://openaccess.thecvf.com/content_cvpr_2015/html/Yi_Understanding_Pedestrian_Behaviors_2015_CVPR_paper.html) | 
 | ![](datasets/HERMES/reference.png) | [HERMES](datasets/HERMES) | Controlled Experiments of Pedestrian Dynamics (Unidirectional and bidirectional flows) <code>#Traj:[?]</code> <code>Coord=world-2D</code> <code>FPS=16</code> | [website](https://www.fz-juelich.de/ias/ias-7/EN/AboutUs/Projects/Hermes/_node.html) [data](https://www.fz-juelich.de/ias/ias-7/EN/Research/Pedestrian_Dynamics-Empiricism/_node.html) | 
 | ![](datasets/Waymo/reference.jpg) | [Waymo](datasets/Waymo) | High-resolution sensor data collected by Waymo self-driving cars <code>#Traj:[?]</code> <code>Coord=2D and 3D</code> <code>FPS=?</code> | [website](https://waymo.com/open/) [github](https://github.com/waymo-research/waymo-open-dataset) | 
-| ![](datasets/KITTI/reference.jpg) | [KITTI](datasets/KITTI) | 6 hours of traffic scenarios. various sensors <code>#Traj:[?]</code> <code>Coord=image-3D + Calib</code> <code>FPS=10</code> |  [website](http://www.cvlibs.net/datasets/kitti/) | 
+| ![](datasets/KITTI/reference.jpg) | [KITTI](datasets/KITTI) | 6 hours of traffic scenarios. Various sensors <code>#Traj:[?]</code> <code>Coord=image-3D + Calib</code> <code>FPS=10</code> |  [website](http://www.cvlibs.net/datasets/kitti/) | 
 | ![](datasets/InD/reference.png) | [inD](datasets/InD) | Naturalistic Trajectories of Vehicles and Vulnerable Road Users Recorded at German Intersections <code>#Traj:[Total=11,500]</code> <code>Coord=world-2D</code> <code>FPS=25</code> | [website](https://www.ind-dataset.com/) [paper](https://arxiv.org/pdf/1911.07602.pdf) | 
 | ![](datasets/L-CAS/reference.png) | [L-CAS](datasets/L-CAS) | Multisensor People Dataset Collected by a Pioneer 3-AT robot <code>#Traj:[?]</code> <code>Coord=0</code> <code>FPS=0</code> | [website](https://lcas.lincoln.ac.uk/wp/research/data-sets-software/l-cas-multisensor-people-dataset/) | 
 | ![](datasets/Edinburgh/reference.jpg) | [Edinburgh](datasets/Edinburgh) | People walking through the Informatics Forum (University of Edinburgh) <code>#Traj:[ped=+92,000]</code> <code>FPS=0</code> | [website](http://homepages.inf.ed.ac.uk/rbf/FORUMTRACKING/) | 
 | ![](datasets/Town-Center/reference.jpg) | [Town Center](datasets/Town-Center) | CCTV video of pedestrians in a busy downtown area in Oxford <code>#Traj:[peds=2,200]</code> <code>Coord=0</code> <code>FPS=0</code> | [website](https://megapixels.cc/datasets/oxford_town_centre/) | 
-| ![](datasets/Wild-Track/reference.jpg) | [Wild Track](datasets/Wild-Track) | surveillance video dataset of students recorded outside the ETH university main building in Zurich. <code>#Traj:[peds=1,200]</code> | [website](https://megapixels.cc/wildtrack/) | 
+| ![](datasets/Wild-Track/reference.jpg) | [Wild Track](datasets/Wild-Track) | Surveillance video dataset of students recorded outside the ETH university main building in Zurich. <code>#Traj:[peds=1,200]</code> | [website](https://megapixels.cc/wildtrack/) | 
 | ![](datasets/ATC/reference.png) | [ATC](datasets/ATC) | 92 days of pedestrian trajectories in a shopping center in Osaka, Japan <code>#Traj:[?]</code> <code>Coord=world-2D + Range data</code> | [website](https://irc.atr.jp/crest2010_HRI/ATC_dataset) | 
 | ![](datasets/VIRAT/reference.png) | [VIRAT](datasets/VIRAT) | Natural scenes showing people performing normal actions <code>#Traj:[?]</code> <code>Coord=0</code> <code>FPS=0</code> | [website](http://viratdata.org/) | 
 | ![](datasets/Forking-Paths-Garden/reference.png) | [Forking Paths Garden](datasets/Forking-Paths-Garden) | **Multi-modal** _Synthetic_ dataset, created in [CARLA](https://carla.org) (3D simulator) based on real world trajectory data, extrapolated by human annotators <code>#Traj:[?]</code> | [website](https://next.cs.cmu.edu/multiverse/index.html) [github](https://github.com/JunweiLiang/Multiverse) [paper](https://arxiv.org/abs/1912.06445) | 
@@ -30,7 +30,7 @@ We introduce existing datasets for Human Trajectory Prediction (HTP) task, and a
 | ![](datasets/City-Scapes/reference.png) | [City Scapes](datasets/City-Scapes) | 25,000 annotated images (Semantic/ Instance-wise/ Dense pixel annotations) <code>#Traj:[?]</code> | [website](https://www.cityscapes-dataset.com/dataset-overview/) | 
 | ![](datasets/Argoverse/reference.jpg) | [Argoverse](datasets/Argoverse) | 320 hours of Self-driving dataset <code>#Traj:[objects=11,052]</code> <code>Coord=3D</code> <code>FPS=10</code> | [website](https://www.argoverse.org) | 
 | ![](datasets/Ko-PER/reference.png) | [Ko-PER](datasets/Ko-PER) | Trajectories of People and vehicles at Urban Intersections (Laserscanner + Video) <code>#Traj:[peds=350]</code> <code>Coord=world-2D</code> | [paper](https://www.uni-ulm.de/fileadmin/website_uni_ulm/iui.inst.110/Bilder/Forschung/Datensaetze/20141010_DatasetDocumentation.pdf) | 
-| ![](datasets/TRAF/reference.png) | [TRAF](datasets/TRAF) | small dataset of dense and heterogeneous traffic videos in India (22 footages) <code>#Traj:[Cars=33 Bikes=20 Peds=11]</code> <code>Coord=image-2D</code> <code>FPS=10</code> | [website](https://gamma.umd.edu/researchdirections/autonomousdriving/trafdataset/) [gDrive](https://drive.google.com/drive/folders/1zKaeboslkqoLdTJbRMyQ0Y9JL3007LRr) [paper](https://arxiv.org/pdf/1812.04767.pdf) | 
+| ![](datasets/TRAF/reference.png) | [TRAF](datasets/TRAF) | Small dataset of dense and heterogeneous traffic videos in India (22 footages) <code>#Traj:[Cars=33 Bikes=20 Peds=11]</code> <code>Coord=image-2D</code> <code>FPS=10</code> | [website](https://gamma.umd.edu/researchdirections/autonomousdriving/trafdataset/) [gDrive](https://drive.google.com/drive/folders/1zKaeboslkqoLdTJbRMyQ0Y9JL3007LRr) [paper](https://arxiv.org/pdf/1812.04767.pdf) | 
 | ![](datasets/ETH-Person/reference.png) | [ETH-Person](datasets/ETH-Person) | Multi-Person Data Collected from Mobile Platforms | [website](https://data.vision.ee.ethz.ch/cvl/aess/) | 
 
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@@ -105,7 +105,7 @@ Final Displacement Error (FDE) measures the distance between final predicted pos
 - Social GAN: Socially Acceptable Trajectories with Generative Adversarial Networks, Gupta et al. CVPR 2018. [paper]()
 - Social Ways: Learning Multi-Modal Distributions of Pedestrian Trajectories with GANs, Amirian et al. CVPR 2019. [paper](), [code]()
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-**References**: an *awsome* list of trajectory prediction references can be found [here](https://github.com/jiachenli94/Awesome-Interaction-aware-Trajectory-Prediction)
+**References**: an *awesome* list of trajectory prediction references can be found [here](https://github.com/jiachenli94/Awesome-Interaction-aware-Trajectory-Prediction)
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 - Desire: Distant future prediction in dynamic scenes with interacting agents, Lee et al. CVPR 2017. [paper](http://openaccess.thecvf.com/content_cvpr_2017/papers/Lee_DESIRE_Distant_Future_CVPR_2017_paper.pdf)
 - Sophie: An attentive gan for predicting paths compliant to social and physical constraints, Sadeghian et al. CVPR 2019. [paper](https://arxiv.org/pdf/1806.01482.pdf)