Abstract
Detecting pedestrians in images and videos plays a critically important role in many computer vision applications. Extraction of effective features is the key to this task. Promising features should be discriminative, robust to various variations and easy to compute. In this work, we presents a novel feature, termed pyramid center-symmetric local binary/ternary patterns (pyramid CS-LBP/LTP), for pedestrian detection. The standard LBP proposed by Ojala et al. [1] mainly captures the texture information. The proposed CS-LBP feature, in contrast, captures the gradient information. Moreover, the pyramid CS-LBP/LTP is easy to implement and computationally efficient, which is desirable for real-time applications. Experiments on the INRIA pedestrian dataset show that the proposed feature outperforms the histograms of oriented gradients (HOG) feature and comparable with the start-of-the-art pyramid HOG (PHOG) feature when using the intersection kernel support vector machines (HIKSVMs). We also demonstrate that the combination of our pyramid CS-LBP feature and the PHOG feature could significantly improve the detection performance—producing state-of-the-art accuracy on the INRIA pedestrian dataset.
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
Ojala, T., Pietikainen, M., Harwood, D.: A comparative study of texture meatures with classification based on featured distribution. Pattern Recogn. 29, 51–59 (1996)
Haritaoglu, I., Harwood, D., Davis, L.S.: W4: real-time surveillance of people and their activities. IEEE Trans. Pattern Anal. Mach. Intell. 22, 809–830 (2000)
Gavrila, D.M., Munder, S.: Multi-cue pedestrian detection and tracking from a moving vehicle. Int. J. Comp. Vis. 73, 41–59 (2007)
abd J. Giebel, D.M.G., Munder, S.: Vision-based pedestrian detection: the protector system. In: Proc. IEEE Int. Conf. Intell. Vehic. Symposium, Parma, Italy, pp. 13–18 (2004)
Nakada, T., Kagami, S., Mizoguchi, H.: Pedestrian detection using 3d optical flow sequences for a mobile robot. In: Proc. IEEE Conf. Sens, Leece, Italy, pp. 776–779 (2008)
Viola, P., Jones, M.J., Snow, D.: Detecting pedestrian using patterns of motion and appearance. In: Proc. IEEE Int. Conf. Comp. Vis., Nice, France, vol. 2, pp. 734–741 (2003)
Mikolajczyk, K., Schmid, C., Zisserman, A.: Human detection based on a probabilistic assembly of robust part detectors. In: Pajdla, T., Matas, J(G.) (eds.) ECCV 2004. LNCS, vol. 3021, pp. 69–82. Springer, Heidelberg (2004)
Dalal, N., Triggs, B.: Histogram of oriented gradients for human detection. In: Proc. IEEE Conf. Comp. Vis. Patt. Recogn., San Diego, USA, vol. 1, pp. 886–893 (2005)
Dalal, N.: Finding people in images and videos. PhD thesis, Institut National Polytechnique de Grenoble (2006)
Tuzel, O., Porikli, F., Meer, P.: Human detection via classification on riemannian manifolds. In: Proc. IEEE Conf. Comp. Vis. Patt. Recogn., Minneapolis, Minnesota, USA, pp. 1–8 (2007)
Munder, S., Gavrila, D.M.: An experimental study on pedestrian classification. IEEE Trans. Pattern Anal. Mach. Intell. 28, 1863–1868 (2006)
Paisitkriangkrai, S., Shen, C., Zhang, J.: Fast pedestrian detection using a cascade of boosted covariance features. IEEE Trans. Circuits Syst. Video Technol. 18, 1140–1151 (2008)
Dollár, P., Wojek, C., Schiele, B., Perona, P.: Pedestrian detection: A benchmark. In: Proc. IEEE Conf. Comp. Vis. Patt. Recogn., pp. 304–311 (2009)
Wang, X., Han, T., Yan, S.: An HoG–LBP human detector with partial occlusion handling. In: Proc. IEEE Int. Conf. Comp. Vis., Kyoto, Japan, pp. 32–39 (2009)
Enzweiler, M., Gavrila, D.M.: Monocular pedestrian detection: Survey and experiments. IEEE Trans. Pattern Anal. Mach. Intell. 31, 2179–2195 (2009)
Maji, S., Berg, A., Malik, J.: Classification using intersection kernel support vector machines is efficient. In: Proc. IEEE Conf. Comp. Vis. Patt. Recogn., Anchorage, Alaska, USA, pp. 1–8 (2008)
Wojek, C., Walk, S., Schiele, B.: Multi-cue onboard pedestrian detection. In: Proc. IEEE Conf. Comp. Vis. Patt. Recogn., Miami, Florida, USA, pp. 794–801 (2009)
Heikkila, M., Schmid, C.: Description of interest regions with local binary patterns. Pattern Recogn. 42, 425–436 (2009)
Ojala, T., Pietikainen, M., Maenpaa, T.: Multiresolution gray-scale and rotation invariant texture classification with local binary patterns. IEEE Trans. Pattern Anal. Mach. Intell. 24, 971–987 (2002)
Ahonen, T., Hadid, A., Pietikainen, M.: Face detection with local binary patterns: application to face recognition. IEEE Trans. Pattern Anal. Mach. Intell. 28, 2037–2041 (2006)
Tan, X., Triggs, B.: Enhanced local texture feature sets for face recognition under difficult lighting conditions. IEEE Trans. Image Process. 19, 1635–1650 (2010)
Bosch, A., Zisserman, A., Muñoz, X.: Scene classification via PLSA. In: Leonardis, A., Bischof, H., Pinz, A. (eds.) ECCV 2006. LNCS, vol. 3954, pp. 517–530. Springer, Heidelberg (2006)
Bosch, A., Zisserman, A., Munoz, X.: Representing shape with a spatial pyramid kernel. In: Proc. ACM. Int. Conf. Image & Video Retrieval, pp. 401–408 (2007)
Gehler, P., Nowozin, S.: On feature combination for multiclass object classification. In: Proc. IEEE Int. Conf. Comp. Vis., pp. 221–228 (2009)
Author information
Authors and Affiliations
Editor information
Editors and Affiliations
Rights and permissions
Copyright information
© 2011 Springer-Verlag Berlin Heidelberg
About this paper
Cite this paper
Zheng, Y., Shen, C., Hartley, R., Huang, X. (2011). Pyramid Center-Symmetric Local Binary/Trinary Patterns for Effective Pedestrian Detection. In: Kimmel, R., Klette, R., Sugimoto, A. (eds) Computer Vision – ACCV 2010. ACCV 2010. Lecture Notes in Computer Science, vol 6495. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-642-19282-1_23
Download citation
DOI: https://doi.org/10.1007/978-3-642-19282-1_23
Publisher Name: Springer, Berlin, Heidelberg
Print ISBN: 978-3-642-19281-4
Online ISBN: 978-3-642-19282-1
eBook Packages: Computer ScienceComputer Science (R0)