Abstract
We mainly focus on feature sharing problem for object detection in cluttered scenes. The contributions are two-fold. First, a novel kind of edge/contour descriptors is presented and they serve as the basic features for sharing. Compared with HOGs (histograms of oriented gradients), the descriptors show the approximately equivalent efficiency while much less computational lost. Second, to exploit feature sharing techniques for object detection, a mathematical representation of shared features for ”sliding-window” based object detection methods is given. Also with the newly defined shared features, a learning framework based on Real-Adaboost algorithm and a reusing framework based on look-up table are proposed. Experimental results show both the efficiency of proposed features and feature sharing method.
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
Preview
Unable to display preview. Download preview PDF.
References
Viola, P., Jones, M.: Rapid object detection using a boosted cascade of simple features. In: CVPR (2001)
Lienhart, Maydt, J.: An Extended Set of Haar-like Features for Rapid Object Detection. In: ICPR 2002 (2002)
Dalal, N., Trigges, B.: Histograms of Oriented Gradients for Human Detection. In: ICCV (2005)
Wu, B., Nevatia, R.: Detection and Tracking of Multiple, Partially Occluded Humans by Bayesian Combination of Edgelet Based Part Detectors. IJCV 75(2), 247–266 (2007)
Li, F.-F., Perona, P.: A bayesian hierarchical model for learning natural scene categories. In: CVPR 2005 (2005)
Leibe, B., Leonardis, A., Schiele, B.: Robust object detection with interleaved categorization and segmentation. In: IJCV (2008)
Gall, J., Lempitsky, V.: Class-specific hough forests for object detection. In: CVPR (2009)
Felzenszwalb, P.F., Girshick, R.B., McAllester, D.A., Ramanan, D.: Object detection with discriminatively trained part-based models. IEEE Trans. Pattern Anal. Mach. Intell. 32(9), 1627–1645 (2010)
Wu, B., Nevatia, R.: Cluster boosted tree classifier for multi-view, multi-pose object detection. In: ICCV (2007)
Schneiderman, H.: Feature-centric evaluation for efficient cascaded object detection. In: CVPR 2004 (2004)
Torralba, A., Murphy, K.P., Freeman, W.T.: Sharing visual features for muticlass and multiview object detection. IEEE Trans. Pattern Anal. Mach. Intell. 29(5), 854–869 (2007)
Agarwal, S., Roth, D.: Learning a Sparse Representation for Object Detection. In: Heyden, A., Sparr, G., Nielsen, M., Johansen, P. (eds.) ECCV 2002, Part IV. LNCS, vol. 2353, pp. 113–127. Springer, Heidelberg (2002)
Fergus, R., Perona, P., Zisserman, A.: Object class recognition by unsupervised scale-invariant learning. In: CVPR 2003 (2003)
Fritz, M., Leibe, B., Caputo, B., Schiele, B.: Integrating representative and discriminant models for object category detection. In: ICCV 2005 (2005)
Villamizar, M., Moreno-Noguer, F., Andrade-Cetto, J., Sanfeliu, A.: Efficient rotation invariant object detection using boosted Random Ferns. In: CVPR 2010 (2010)
Papageorgiou, C., Poggio, T.: A trainable system for object detection. Int’l J. Computer Vision 38(1), 15–33 (2000)
Everingham, M., Zisserman, A., Williams, C., Van Gool, L.: The PASCAL Visual Object Classes Challenge 2006 (VOC 2006) Results (2006), http://www.pascal-network.org/challenges/VOC/voc2006/
Everingham, M., Van Gool, L., Williams, C.K.I., Winn, J., Zisserman, A.: The PASCAL Visual Object Classes Challenge 2007 (VOC 2007) Results (2007), http://www.pascal-network.org/challenges/VOC/voc2007/
Leibe, B., Cornelis, N., Cornelis, K., Van Gool, L.: Dynamic 3d scene analysis from a moving vehicle. In: CVPR 2007 (2007)
Author information
Authors and Affiliations
Editor information
Editors and Affiliations
Rights and permissions
Copyright information
© 2013 Springer-Verlag Berlin Heidelberg
About this paper
Cite this paper
Li, Y., He, F., Lu, W., Wang, S. (2013). Combining Fast Extracted Edge Descriptors and Feature Sharing for Rapid Object Detection. In: Park, JI., Kim, J. (eds) Computer Vision - ACCV 2012 Workshops. ACCV 2012. Lecture Notes in Computer Science, vol 7729. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-642-37484-5_39
Download citation
DOI: https://doi.org/10.1007/978-3-642-37484-5_39
Publisher Name: Springer, Berlin, Heidelberg
Print ISBN: 978-3-642-37483-8
Online ISBN: 978-3-642-37484-5
eBook Packages: Computer ScienceComputer Science (R0)