Abstract
The most relevant modules of a pedestrian detector are the candidate generation and the candidate classification. The former aims at presenting image windows to the latter so that they are classified as containing a pedestrian or not. Much attention has being paid to the classification module, while candidate generation has mainly relied on (multiscale) sliding window pyramid. However, candidate generation is critical for achieving real-time. In this paper we assume a context of autonomous driving based on stereo vision. Accordingly, we evaluate the effect of taking into account the 3D information (derived from the stereo) in order to prune the hundred of thousands windows per image generated by classical pyramidal sliding window. For our study we use a multi-modal (RGB, disparity) and multi-descriptor (HOG, LBP, HOG+LBP) holistic ensemble based on linear SVM. Evaluation on data from the challenging KITTI benchmark suite shows the effectiveness of using 3D information to dramatically reduce the number of candidate windows, even improving the overall pedestrian detection accuracy.
This is a preview of subscription content, log in via an institution to check access.
Access this chapter
Tax calculation will be finalised at checkout
Purchases are for personal use only
References
Alonso, I.P., Llorca, D.F., Sotelo, M.A., Bergasa, L.M., de Toro, P.R., Nuevo, J., Ocana, M., Garrido, M.A.: Combination of feature extraction methods for svm pedestrian detection. Trans. Intell. Transport. Sys. 8(2), 292–307 (2007)
Benenson, R., Mathias, M., Timofte, R., Van Gool, L.: Pedestrian detection at 100 frames per second. In: Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, Providence, RI, USA (2012)
Dollár, P., Tu, Z., Perona, P., Belongie, S.: Integral channel features. In: Proceedings of the British Machine Vision Conference, London, UK (2009)
Felzenszwalb, P., Girshick, R., McAllester, D., Ramanan, D.: Object detection with discriminatively trained part based models. IEEE Trans. Pattern Anal. Mach. Intell. 32(9), 1627–1645 (2010)
Geiger, A., Lenz, P., Urtasun, R.: Are we ready for autonomous driving? the kitti vision benchmark suite. In: CVPR 2012 (2012)
Gerónimo, D., López, A.: Vision-based Pedestrian Protection Systems for Intelligent Vehicles. Springer Briefs in Computer Science. Springer, New York (2013)
Gerónimo, D., Sappa, A., Ponsa, D., López, A.: 2D–3D based on-board pedestrian detection system. J. Comput. Vis. Image Underst. 114(5), 583–595 (2010)
Gu, C., Lim, J.J., Arbelez, P., Malik, J.: Recognition using regions. In: Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition (2009)
Hosang, J., Benenson, R., Schiele, B.: How good are detection proposals, really? In: Proceedings of the British Machine Vision Conference (2014)
Labayrade, R., Aubert, D., Tarel, J.P.: Real time obstacle detection in stereovision on non flat road geometry through “v-disparity" representation. In: IEEE Intelligent Vehicle Symposium (2002)
Marin, J., Vázquez, D., López, A., Amores, J., Leibe, B.: Random forests of local experts for pedestrian detection. In: Proceedings of the IEEE International Conference on Computer Vision (2013)
Ros, G., Ramos, S., Granados, M., Bakhtiary, A., Vazquez, D., Lopez, A.: Vision-based offline-online paradigm for autonomous driving. In: Winter Conference on Applications of Computer Vision (WACV) (2015)
Uijlings, J.R.R., van de Sande, K.E.A., Gevers, T., Smeulders, A.W.M.: Selective search for object recognition. Int. J. Comput. Vision 104(2), 154–171 (2013)
van de Sande, K.E.A., Uijlings, J.R.R., Gevers, T., Smeulders, A.W.M.: Segmentation as selective search for object recognition. In: Proceedings of the IEEE International Conference on Computer Vision (2011)
Wang, X., Han, T.X., Yan, S.: An HOG-LBP human detector with partial occlusion handling. In: Proceedings of the IEEE International Conference on Computer Vision, Kyoto, Japan (2009)
Zitnick, C.L., Dollár, P.: Edge boxes: locating object proposals from edges. In: Fleet, D., Pajdla, T., Schiele, B., Tuytelaars, T. (eds.) ECCV 2014, Part V. LNCS, vol. 8693, pp. 391–405. Springer, Heidelberg (2014)
Acknowledgments
This work is supported by the Spanish MICINN projects TRA2011-29454-C03-01 and TIN2011-29494-C03-02.
Author information
Authors and Affiliations
Corresponding author
Editor information
Editors and Affiliations
Rights and permissions
Copyright information
© 2015 Springer International Publishing Switzerland
About this paper
Cite this paper
González, A., Villalonga, G., Ros, G., Vázquez, D., López, A.M. (2015). 3D-Guided Multiscale Sliding Window for Pedestrian Detection. In: Paredes, R., Cardoso, J., Pardo, X. (eds) Pattern Recognition and Image Analysis. IbPRIA 2015. Lecture Notes in Computer Science(), vol 9117. Springer, Cham. https://doi.org/10.1007/978-3-319-19390-8_63
Download citation
DOI: https://doi.org/10.1007/978-3-319-19390-8_63
Published:
Publisher Name: Springer, Cham
Print ISBN: 978-3-319-19389-2
Online ISBN: 978-3-319-19390-8
eBook Packages: Computer ScienceComputer Science (R0)