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 Article in proceedingAcademicpeer-review


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 25th IEEE International Conference on Image Processing (ICIP)
Subtitle of host publicationProceedings
Number of pages5
ISBN (Electronic)978-1-4799-7061-2
ISBN (Print)978-1-4799-7062-9
Publication statusPublished - 29 Aug 2018
Externally publishedYes
Event25th IEEE International Conference on Image Processing - Athens, Greece
Duration: 7 Oct 201810 Oct 2018


Conference25th IEEE International Conference on Image Processing
Abbreviated titleICIP 2018
Internet address


  • Deep learning
  • Lidar
  • Pedestrian detection


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