Synthetic Crowd And Pedestrian Generator For Deep Learning Problems

Deep Neural networks (DNN) dominate the state of art results in computer vision (CV) and other fields. One of the primary reasons why DNN outperform existing algorithms is that these produce superior results when more labelled data are used, unlike classic CV techniques. Nonetheless, it is well known that DNN requires a very large amount of data to generalise well. Collecting and labelling these datasets are expensive, time-consuming and sometimes impossible. Therefore, researchers tried to use alternative techniques, such as graphics simulators to automatically generate labelled datasets. However, these techniques are still expensive and require domain knowledge to produce good datasets. In this paper, therefore, a graphics simulator is presented which automatically generates multi-model datasets in real-time providing the corresponding ground truth and annotation. The tool concentrates on pedestrian and crowd analysis including 3D human pose estimation, pedestrian detection as well as crowd density and flow estimation.