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Pedestrian detection algorithm based on video sequences and laser point cloud

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Abstract

Pedestrian detection is a critical problem in the field of computer vision. Although most existing algorithms are able to detect pedestrians well in controlled environments, it is often difficult to achieve accurate pedestrian detection from video sequences alone, especially in pedestrianintensive scenes wherein pedestrians may cause mutual occlusion and thus incomplete detection. To surmount these difficulties, this paper presents pedestrian detection algorithm based on video sequences and laser point cloud. First, laser point cloud is interpreted and classified to separate pedestrian data and vehicle data. Then a fusion of video image data and laser point cloud data is achieved by calibration. The region of interest after fusion is determined using feature information contained in video image and three-dimensional information of laser point cloud to remove false detection of pedestrian and thus to achieve pedestrian detection in intensive scenes. Experimental verification and analysis in video sequences demonstrate that fusion of two data improves the performance of pedestrian detection and has better detection results.

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Authors and Affiliations

  1. School of Information Science and Technology, Qingdao University of Science and Technology, Qingdao, 266061, China

    Hui Li & Yun Liu

  2. School of Computer Science, Wuhan University of Technology, Wuhan, 430070, China

    Shengwu Xiong & Lin Wang

Authors
  1. Hui Li
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  2. Yun Liu
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  3. Shengwu Xiong
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  4. Lin Wang
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Corresponding author

Correspondence to Hui Li.

Additional information

Hui Li received his MS and PhD degrees in computer science and technology from Wuhan University of Technology, China in 2010 and 2013, respectively. He is currently a lecturer in the School of Information Science and Technology, Qingdao University of Science and Technology, China. His current research interests cover object tracking algorithms, image processing, and localization algorithms.

Yun Liu received his MS degree from Tongji University in 1995 and PhD degree from China University of Mining and Technology, Beijing in 2001. He is currently a professor in the School of Information Science and Technology, Qingdao University of Science and Technology, China. His current research interests include image processing and virtual reality technology.

Shengwu Xiong received his MS and PhD degrees in computer software and theory from Wuhan University, China. He is currently a professor in the School of Computer Science, Wuhan University of Science and Technology, China. His research interests include intelligent computing, machine learning and pattern recognition.

Lin Wang received his MS degree in computer science from Wuhan University of Technology, China in 2014. His research interests include interpretation and detection algorithm of laser point cloud. He now works for Tencent, China.

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Li, H., Liu, Y., Xiong, S. et al. Pedestrian detection algorithm based on video sequences and laser point cloud. Front. Comput. Sci. 9, 402–414 (2015). https://doi.org/10.1007/s11704-014-3413-2

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  • DOI: https://doi.org/10.1007/s11704-014-3413-2

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