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2017 14th IEEE International Conference on Advanced Video and Signal Based Surveillance (AVSS) (2017)
Lecce, Italy
Aug. 29, 2017 to Sept. 1, 2017
ISBN: 978-1-5386-2940-6
pp: 1-6
Damien Matti , LTS5, EPFL, Lausanne, Switzerland
Hazim Kemal Ekenel , LTS5, EPFL, Lausanne, Switzerland
Jean-Philippe Thiran , LTS5, EPFL, Lausanne, Switzerland
ABSTRACT
Pedestrian detection is an important component for safety of autonomous vehicles, as well as for traffic and street surveillance. There are extensive benchmarks on this topic and it has been shown to be a challenging problem when applied on real use-case scenarios. In purely image-based pedestrian detection approaches, the state-of-the-art results have been achieved with convolutional neural networks (CNN) and surprisingly few detection frameworks have been built upon multi-cue approaches. In this work, we develop a new pedestrian detector for autonomous vehicles that exploits LiDAR data, in addition to visual information. In the proposed approach, LiDAR data is utilized to generate region proposals by processing the three dimensional point cloud that it provides. These candidate regions are then further processed by a state-of-the-art CNN classifier that we have fine-tuned for pedestrian detection. We have extensively evaluated the proposed detection process on the KITTI dataset. The experimental results show that the proposed LiDAR space clustering approach provides a very efficient way of generating region proposals leading to higher recall rates and fewer misses for pedestrian detection. This indicates that LiDAR data can provide auxiliary information for CNN-based approaches.
INDEX TERMS
Laser radar, Proposals, Three-dimensional displays, Visualization, Sensors, Clustering algorithms, Neural networks
CITATION

D. Matti, H. K. Ekenel and J. Thiran, "Combining LiDAR space clustering and convolutional neural networks for pedestrian detection," 2017 14th IEEE International Conference on Advanced Video and Signal Based Surveillance (AVSS), Lecce, Italy, 2017, pp. 1-6.
doi:10.1109/AVSS.2017.8078512
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