Pedestrian Detection using RGB-D Information and Distance Transform

RGB-D 정보 및 거리변환을 이용한 보행자 검출

  • Received : 2016.02.10
  • Accepted : 2016.02.24
  • Published : 2016.03.01


According to the development of depth sensing devices and depth estimation technology, depth information becomes more important for object detection in computer vision. In terms of recognition rate, pedestrian detection methods have been improved more accurately. However, the methods makes slower detection time. So, many researches have overcome this problem by using GPU. Here, we propose a real-time pedestrian detection algorithm that does not rely on GPU. First, the depth-weighted distance map is used for detecting expected human regions. Next, human detection is performed on the regions. The performance for the proposed approach is evaluated and compared with the previous methods. We show that proposed method can detect human about 7 times faster than conventional ones.


Pedestrian detection;RGB-D;Distance transform


  1. N. Dalal, and B. Triggs, "Histograms of Oriented Gradients for Human Detection," IEEE Conf. on. Computer Vision and Pattern Recognition, Vol. 1, pp. 886-893, 2005.
  2. P. Viola, and M. Jones, "Rapid Object Detection using a Boosted Cascade of Simple Features," IEEE Conference on Computer Vision and Pattern Recognition, Vol. 1, pp. 511-518, 2001.
  3. R. Benenson, M. Mathias, J. Hosang, and B. Schiele, "Ten Years of Pedestrian Detection, What Have We Learned," In European Confernce on Computer Vision Workshop, 2014.
  4. P. Dollar, and Z. Tu, "Intergral Channel features," In British Machine Vision Conference, 2009.
  5. A. Satpathy, and X.Jiang, "Human detection by quadratic classification on subspcae of extended histogram of gradients," IEEE Transactions on Image Processing, Vol. 3, No. 1, pp. 287-297, 2014.
  6. Jae-Do Kim, "Fast Pedestrian Detection Using Estimation of Feature information Based on Integral Image," Journal of IKEEE., Vol. 17, No. 4, pp. 469-477, 2013.
  7. Byeong-Ju Park, "High Efficient Viola-Jones Detection Framework for Real-Time Object Detection," Journal of IKEEE, Vol. 18, No. 1, pp. 1-7,2014.
  8. Jun Liu, "Real-time Human Detection and Tracking in Complex Environments using Single RGB-D Camera," IEEE International Conference on Image Processing, pp. 3088-3092, 2013.
  9. Yujie Shen, "A Novel Human Detection Approach Based on Depth Map via Kinect," IEEE Conference on Computer Vision and Pattern Recognition Workshops, pp. 535-541, 2013.
  10. R. Kimmel, N. Kiryati, and A. M. Bruckstein, "Distance maps and Weighted Distance Transforms," Journal of Mathematical Imaging and Vision, Vol. 6, pp. 223-233, 1996.
  11. G. Borgefors, "Distance Transformations in Digital Images," Computer Vision, Graphics, and Image Processing, Vol. 34, pp. 344-371, 1986.
  12. G. Borgefors, "Distance Transformations in Arbitrary Dimensions," Computer Vision, Graphics, and Image Processing, Vol. 27, pp. 321-345, 1984.
  13. L. Spinello, "People Detection in RGB-D Data," IEEE International Conference on Intelligent Robot and System, pp. 3838-3843, 2011.


Supported by : 한국연구재단