Pedestrian Detection from Lidar Data via Cooperative Deep and Hand-Crafted Features

Tzu Chieh Lin, Daniel Stanley Tan, Hsueh Ling Tang, Shih Che Chien, Feng Chia Chang, Yung Yao Chen, Wen-Huang Cheng, Kai Lung Hua

Research output: Chapter in Book/Report/Conference proceedingConference contribution

6 Scopus citations

Abstract

Autopilot systems need to be able to detect pedestrians with high precision and recall regardless of whether it is during the day or night. This means that we cannot rely on normal cameras to sense the surroundings due to its sensitivity to lighting conditions. An alternative for images is to use light detection and ranging sensors (LiDAR) that produces three-dimensional point clouds where each point represents the distance to an object. However, most pedestrian detection systems are designed for image inputs and not on distance point clouds. In this paper, we propose a method for detecting pedestrians using only the three-dimensional point clouds generated by the LiDAR. Our approach first projects the three-dimensional point cloud into a two-dimensional plane. We then extract both hand-crafted features and learned features from a convolutional neural network in order to train a support vector machine (SVM) to detect pedestrians. Our proposed method achieved significant improvements in terms of F1-measurement over prior state-of-the-art methods.

Original languageEnglish
Title of host publication2018 IEEE International Conference on Image Processing, ICIP 2018 - Proceedings
PublisherIEEE Computer Society
Pages1922-1926
Number of pages5
ISBN (Electronic)9781479970612
DOIs
StatePublished - 29 Aug 2018
Event25th IEEE International Conference on Image Processing, ICIP 2018 - Athens, Greece
Duration: 7 Oct 201810 Oct 2018

Publication series

NameProceedings - International Conference on Image Processing, ICIP
ISSN (Print)1522-4880

Conference

Conference25th IEEE International Conference on Image Processing, ICIP 2018
CountryGreece
CityAthens
Period7/10/1810/10/18

Keywords

  • Deep learning
  • Lidar
  • Pedestrian detection

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  • Cite this

    Lin, T. C., Tan, D. S., Tang, H. L., Chien, S. C., Chang, F. C., Chen, Y. Y., Cheng, W-H., & Hua, K. L. (2018). Pedestrian Detection from Lidar Data via Cooperative Deep and Hand-Crafted Features. In 2018 IEEE International Conference on Image Processing, ICIP 2018 - Proceedings (pp. 1922-1926). [8451578] (Proceedings - International Conference on Image Processing, ICIP). IEEE Computer Society. https://doi.org/10.1109/ICIP.2018.8451578