Superpixels in Pedestrian Detection from Stereo Images in Urban Traffic Scenarios

Ion Giosan, Sergiu Nedevschi

2016

Abstract

Pedestrian detection is a common task in every driving assistance system. The main goal resides in obtaining a high accuracy detection in a reasonable amount of processing time. This paper proposes a novel method for superpixel-based pedestrian hypotheses generation and their validation through feature classification. We analyze the possibility of using superpixels in pedestrian detection by investigating both the execution time and the accuracy of the results. Urban traffic images are acquired by a stereo-cameras system. A multi-feature superpixels-based method is used for obstacles segmentation and pedestrian hypotheses selection. Histogram of Oriented Gradients features are extracted both on the raw 2D intensity image and also on the superpixels mean intensity image for each hypothesis. Principal Component Analysis is also employed for selecting the relevant features. Support Vector Machine and AdaBoost classifiers are trained on: initial features and selected features extracted from both raw 2D intensity image and mean superpixels intensity image. The comparative results show that superpixels- based pedestrian detection clearly reduce the execution time while the quality of the results is just slightly decreased.

References

  1. Achanta, R., Shaji, A., Smith, K., Lucchi, A., Fua, P. & Susstrunk, S. 2012. SLIC Superpixels Compared to State-of-the-Art Superpixel Methods. IEEE Transactions on Pattern Analysis and Machine Intelligence, 34, pp. 2274-2282.
  2. Benenson, R., Mathias, M., Timofte, R. & Van Gool, L. 2012. Pedestrian detection at 100 frames per second. IEEE Conference on Computer Vision and Pattern Recognition (CVPR). pp. 2903-2910.
  3. Bertozzi, M., Bombini, L., Cerri, P., Medici, P., Antonello, P. C. & Miglietta, M. 2008. Obstacle detection and classification fusing radar and vision. IEEE Intelligent Vehicles Symposium. pp. 608-613.
  4. Bertozzi, M. & Broggi, A. 1998. GOLD: a parallel real-time stereo vision system for generic obstacle and lane detection. IEEE Transactions on Image Processing, 7, pp. 62-81.
  5. Bertozzi, M., Broggi, A., Fascioli, A. & Nichele, S. 2000. Stereo vision-based vehicle detection. Proceedings of the IEEE Intelligent Vehicles Symposium. pp. 39-44.
  6. Broggi, A., Bertozzi, M., Fascioli, A. & Sechi, M. 2000. Shape-based pedestrian detection. IEEE Intelligent Vehicles Symposium. pp. 215-220.
  7. Broggi, A., Buzzoni, M., Felisa, M. & Zani, P. 2011. Stereo obstacle detection in challenging environments: The VIAC experience. International Conference on Intelligent Robots and Systems (IROS). pp. 1599-1604.
  8. Dalal, N. & Triggs, B. 2005. Histograms of oriented gradients for human detection. IEEE Computer Society Conference on Computer Vision and Pattern Recognition. pp. 886-893 vol. 1.
  9. Felzenszwalb, P. F. & Huttenlocher, D. P. 2004. Efficient Graph-Based Image Segmentation. International Journal of Computer Vision, 59, pp. 167-181.
  10. Gavrila, D. 2000. Pedestrian Detection from a Moving Vehicle. Proceedings of the 6th European Conference on Computer Vision-Part II, pp. 37-49.
  11. Gavrila, D. M. & Philomin, V. 1999. Real-time object detection for "smart" vehicles. Proceedings of the Seventh IEEE International Conference on Computer Vision. pp. 87-93 vol.1.
  12. Giosan, I. & Nedevschi, S. 2009. Building Pedestrian Contour Hierarchies for Improving Detection in Traffic Scenes. Computer Vision and Graphics - Lecture Notes in Computer Science, pp. 154-163.
  13. Giosan, I. & Nedevschi, S. 2012. A solution for probabilistic inference and tracking of obstacles classification in urban traffic scenarios. IEEE International Conference on Intelligent Computer Communication and Processing (ICCP). pp. 221-227.
  14. Giosan, I. & Nedevschi, S. 2014. Superpixel-based obstacle segmentation from dense stereo urban traffic scenarios using intensity, depth and optical flow information. IEEE 17th International Conference on Intelligent Transportation Systems (ITSC). pp. 1662-1668.
  15. Havasi, L., Szlavik, Z. & Sziranyi, T. 2004. Pedestrian Detection Using Derived Third-Order Symmetry of Legs. Proceedings of the IEEE International Conference on Computer Vision and Graphics, pp. 11- 17.
  16. Heikkila, M. & Pietikainen, M. 2006. A Texture-Based Method for Modeling the Background and Detecting Moving Objects. IEEE Transactions on Pattern Analysis and Machine Intelligence, 28, pp. 657-662.
  17. Hilario, C., Collado, J. M., Armingol, J. M. & Escalera, A. D. L. 2005. Pedestrian detection for intelligent vehicles based on active contour models and stereo vision. Proceedings of the 10th international conference on Computer Aided Systems Theory, pp. 537-542.
  18. Keller, C. G., Enzweiler, M. & Gavrila, D. M. 2011. A new benchmark for stereo-based pedestrian detection. IEEE Intelligent Vehicles Symposium (IV). pp. 691-696.
  19. Llorca, D. F., Sotelo, M. A., Hellín, A. M., Orellana, A., Gavilan, M., Daza, I. G. & Lorente, A. G. 2012. Stereo regions-of-interest selection for pedestrian protection: A survey. Transportation research part C: emerging technologies, 25, pp. 226-237.
  20. Lun, Z., Li, S. Z., Xiaotong, Y. & Shiming, X., 2007. Realtime Object Classification in Video Surveillance Based on Appearance Learning. IEEE Conference on Computer Vision and Pattern Recognition. pp. 1-8.
  21. Nedevschi, S., Bota, S. & Tomiuc, C., 2009. Stereo-Based Pedestrian Detection for Collision-Avoidance Applications. IEEE Transactions on Intelligent Transportation Systems, 10, pp. 380-391.
  22. Nedevschi, S., Danescu, R., Frentiu, D., Marita, T., Oniga, F., Pocol, C., Schmidt, R. & Graf, T. 2004. High accuracy stereo vision system for far distance obstacle detection. IEEE Intelligent Vehicles Symposium. pp. 292-297.
  23. Oniga, F. & Nedevschi, S. 2010. Processing Dense Stereo Data Using Elevation Maps: Road Surface, Traffic Isle, and Obstacle Detection. IEEE Transactions on Vehicular Technology, 59, pp. 1172-1182.
  24. Pantilie, C. D. & Nedevschi, S. 2012. SORT-SGM: Subpixel Optimized Real-Time Semiglobal Matching for Intelligent Vehicles. IEEE Transactions on Vehicular Technology, 61, pp. 1032-1042.
  25. Papageorgiou, C. & Poggio, T. 2000. A Trainable System for Object Detection. International Journal of Computer Vision, 38, pp. 15-33.
  26. Pocol, C., Nedevschi, S. & Meinecke, M. M. 2008. Obstacle Detection Based on Dense Stereovision for Urban ACC Systems. International Workshop on Intelligent Transportation (WIT), Hamburg, Germany. pp. 13-18.
  27. Rivlin, E., Rudzsky, M., Goldenberg, R., Bogomolov, U. & Lepchev, S. 2002. A real-time system for classification of moving objects. 16th International Conference on Pattern Recognition. pp. 688-691 vol.3.
  28. Shi, J. & Tomasi, C. 1994. Good features to track. IEEE Conference on Computer Vision and Pattern Recognition. pp. 593-600.
  29. Toth, D. & Aach, T. 2003. Detection and recognition of moving objects using statistical motion detection and Fourier descriptors. 12th International Conference on Image Analysis and Processing. pp. 430-435.
  30. Xiaofeng, R. & Malik, J. 2003. Learning a classification model for segmentation. Proceedings of IEEE International Conference on Computer Vision. pp. 10- 17 vol.1.
  31. Yi, T., Wei-Ming, L. & Wu, J. 2010. An improved pedestrian detection algorithm based on Adaboost cascading stucture. Proceedings of World Congress on Intelligent Control and Automation (WCICA). pp. 6321-6326.
  32. You, L. & Ruichek, Y. 2012. Moving objects detection and recognition using sparse spatial information in urban environments. IEEE Intelligent Vehicles Symposium. pp. 1060-1065.
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Paper Citation


in Harvard Style

Giosan I. and Nedevschi S. (2016). Superpixels in Pedestrian Detection from Stereo Images in Urban Traffic Scenarios . In Proceedings of the 11th Joint Conference on Computer Vision, Imaging and Computer Graphics Theory and Applications - Volume 4: VISAPP, (VISIGRAPP 2016) ISBN 978-989-758-175-5, pages 501-508. DOI: 10.5220/0005680305010508


in Bibtex Style

@conference{visapp16,
author={Ion Giosan and Sergiu Nedevschi},
title={Superpixels in Pedestrian Detection from Stereo Images in Urban Traffic Scenarios},
booktitle={Proceedings of the 11th Joint Conference on Computer Vision, Imaging and Computer Graphics Theory and Applications - Volume 4: VISAPP, (VISIGRAPP 2016)},
year={2016},
pages={501-508},
publisher={SciTePress},
organization={INSTICC},
doi={10.5220/0005680305010508},
isbn={978-989-758-175-5},
}


in EndNote Style

TY - CONF
JO - Proceedings of the 11th Joint Conference on Computer Vision, Imaging and Computer Graphics Theory and Applications - Volume 4: VISAPP, (VISIGRAPP 2016)
TI - Superpixels in Pedestrian Detection from Stereo Images in Urban Traffic Scenarios
SN - 978-989-758-175-5
AU - Giosan I.
AU - Nedevschi S.
PY - 2016
SP - 501
EP - 508
DO - 10.5220/0005680305010508