10
views
0
recommends
+1 Recommend
0 collections
    0
    shares
      • Record: found
      • Abstract: found
      • Article: found
      Is Open Access

      Driving in the Matrix: Can Virtual Worlds Replace Human-Generated Annotations for Real World Tasks?

      Preprint

      Read this article at

      Bookmark
          There is no author summary for this article yet. Authors can add summaries to their articles on ScienceOpen to make them more accessible to a non-specialist audience.

          Abstract

          Deep learning has rapidly transformed the state of the art algorithms used to address a variety of problems in computer vision and robotics. These breakthroughs have however relied upon massive amounts of human annotated training data. This time-consuming process has begun impeding the progress of these deep learning efforts. This paper describes a method to incorporate photo-realistic computer images from a simulation engine to rapidly generate annotated data that can be used for training of machine learning algorithms. We demonstrate that a state of the art architecture, which is trained only using these synthetic annotations, performs better than the identical architecture trained on human annotated real-world data, when tested on the KITTI data set for vehicle detection. By training machine learning algorithms on a rich virtual world, this paper illustrates that real objects in real scenes can be learned and classified using synthetic data. This approach offers the possibility of accelerating deep learning's application to sensor based classification problems like those that appear in self-driving cars.

          Related collections

          Author and article information

          Journal
          2016-10-06
          Article
          1610.01983
          7b0817fb-9e7e-4541-9cb4-130f3677112a

          http://arxiv.org/licenses/nonexclusive-distrib/1.0/

          History
          Custom metadata
          8 pages
          cs.CV cs.RO

          Computer vision & Pattern recognition,Robotics
          Computer vision & Pattern recognition, Robotics

          Comments

          Comment on this article