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About the Dataset

Deep learning-based methods for video pedestrian detection and tracking require large volumes of training data to achieve good performance.

However, data acquisition in crowded urban environments raises data privacy concerns, as we are not allowed to simply record and store data without the explicit consent of all participants. Furthermore, the annotation of such data for computer vision applications usually requires a substantial amount of manual effort, especially in the video domain. Labeling instances of pedestrians in highly crowded scenarios can be challenging even for human annotators and may introduce errors in the training data.

To this end, we generate PREVUE Synth Dataset, a large, highly diverse synthetic dataset for pedestrian detection and tracking using a rendering game engine.
 

Download the Dataset

The link to download the dataset will be published soon. Check this page to stay updated!
 

Citation

PREVUE Synth Dataset is a subset of MOTSynth Dataset published in the International Conference on Computer Vision 2021.
If you use our dataset, please cite the following work.
 

 @inproceedings{fabbri21iccv,
   title     = {MOTSynth: How Can Synthetic Data Help Pedestrian Detection and Tracking?},
   author    = {Matteo Fabbri and Guillem Bras{\'o} and Gianluca Maugeri and Aljo{\v{s}}a O{\v{s}}ep and 
                Riccardo Gasparini and Orcun Cetintas and Simone Calderara and Laura Leal-Taix{\'e} and Rita Cucchiara},
   booktitle = {International Conference on Computer Vision (ICCV)},
   year      = {2021}
 }