Multispectral Pedestrian Detection using Deep Fusion Convolutional Neural Networks

Jörg Wagner, Volker Fischer, Michael Herman, Sven Behnke

European Symposium on Artificial Neural Networks, Computational Intelligence and Machine Learning, 2016, Bruges, Belgium, April 27 – 29, 2016

Abstract:

Robust vision-based pedestrian detection is a crucial feature of future autonomous systems. Thermal cameras provide an additional input channel that helps solving this task and deep convolutional networks are the currently leading approach for many pattern recognition problems, including object detection. In this paper, we explore the potential of deep models for multispectral pedestrian detection. We investigate two deep fusion architectures and analyze their performance on multispectral data. Our results show that a pre-trained late-fusion architecture significantly outperforms the current state-of-the-art ACF+T+THOG solution.

PDF Bibtex

@INPROCEEDINGS{Wagner2016ESANN,
  author={Jörg Wagner and Volker Fischer and Michael Herman and Sven Behnke},
  booktitle={24th European Symposium on Artificial Neural Networks, Computational Intelligence and Machine Learning (ESANN)},
  title={Multispectral Pedestrian Detection using Deep Fusion Convolutional Neural Networks},
  year={2016},
  pages={509-514},
  month={April},
}