Learning Safety Equipment Detection using Virtual Worlds

@article{DiBenedetto2019LearningSE,
  title={Learning Safety Equipment Detection using Virtual Worlds},
  author={Marco Di Benedetto and Enrico Meloni and Giuseppe Amato and F. Falchi and Claudio Gennaro},
  journal={2019 International Conference on Content-Based Multimedia Indexing (CBMI)},
  year={2019},
  pages={1-6}
}
Nowadays, the possibilities offered by state-of-the-art deep neural networks allow the creation of systems capable of recognizing and indexing visual content with very high accuracy. Performance of these systems relies on the availability of high quality training sets, containing a large number of examples (e.g. million), in addition to the the machine learning tools themselves. For several applications, very good training sets can be obtained, for example, crawling (noisily) annotated images… 

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References

SHOWING 1-10 OF 26 REFERENCES

Unsupervised domain adaptation of virtual and real worlds for pedestrian detection

The transductive SVM (T-SVM) learning algorithm is explored in order to adapt virtual and real worlds for pedestrian detection and the use of unsupervised domain adaptation techniques that avoid human intervention during the adaptation process is proposed.

Driving in the Matrix: Can virtual worlds replace human-generated annotations for real world tasks?

A method to incorporate photo-realistic computer images from a simulation engine to rapidly generate annotated data that can be used for the training of machine learning algorithms, which offers the possibility of accelerating deep learning's application to sensor-based classification problems like those that appear in self-driving cars.

Virtual and Real World Adaptation for Pedestrian Detection

A domain adaptation framework, V-AYLA, in which different techniques to collect a few pedestrian samples from the target domain and combine them with the many examples of the source domain in order to train a domain adapted pedestrian classifier that will operate in thetarget domain.

Training Deep Networks with Synthetic Data: Bridging the Reality Gap by Domain Randomization

This work presents a system for training deep neural networks for object detection using synthetic images that relies upon the technique of domain randomization, in which the parameters of the simulator are randomized in non-realistic ways to force the neural network to learn the essential features of the object of interest.

VIVID: Virtual Environment for Visual Deep Learning

A new Virtual Environment for Visual Deep Learning (VIVID) is presented, which offers large-scale diversified indoor and outdoor scenes and leverages the advanced human skeleton system, which enables us to simulate numerous complex human actions.

Learning appearance in virtual scenarios for pedestrian detection

Detecting pedestrians in images is a key functionality to avoid vehicle-to-pedestrian collisions. The most promising detectors rely on appearance-based pedestrian classifiers trained with labelled

Playing for Data: Ground Truth from Computer Games

It is shown that associations between image patches can be reconstructed from the communication between the game and the graphics hardware, which enables rapid propagation of semantic labels within and across images synthesized by the game, with no access to the source code or the content.

Beyond Grand Theft Auto V for Training, Testing and Enhancing Deep Learning in Self Driving Cars

The efficacy and flexibility of a "GTA-V"-like virtual environment is expected to provide an efficient well-defined foundation for the training and testing of Convolutional Neural Networks for safe driving.

Training a convolutional neural network for multi-class object detection using solely virtual world data

This work developed a CNN-based multi-class detection system that was trained solely on virtual world data and achieves competitive results compared to state-of-the-art detection systems.

The Pascal Visual Object Classes Challenge: A Retrospective

A review of the Pascal Visual Object Classes challenge from 2008-2012 and an appraisal of the aspects of the challenge that worked well, and those that could be improved in future challenges.