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Fused Structure and Texture (FST) Features for Improved Pedestrian Detection

Authors: Hussin K. Ragb, Vijayan K. Asari


In this paper, we present a pedestrian detection descriptor called Fused Structure and Texture (FST) features based on the combination of the local phase information with the texture features. Since the phase of the signal conveys more structural information than the magnitude, the phase congruency concept is used to capture the structural features. On the other hand, the Center-Symmetric Local Binary Pattern (CSLBP) approach is used to capture the texture information of the image. The dimension less quantity of the phase congruency and the robustness of the CSLBP operator on the flat images, as well as the blur and illumination changes, lead the proposed descriptor to be more robust and less sensitive to the light variations. The proposed descriptor can be formed by extracting the phase congruency and the CSLBP values of each pixel of the image with respect to its neighborhood. The histogram of the oriented phase and the histogram of the CSLBP values for the local regions in the image are computed and concatenated to construct the FST descriptor. Several experiments were conducted on INRIA and the low resolution DaimlerChrysler datasets to evaluate the detection performance of the pedestrian detection system that is based on the FST descriptor. A linear Support Vector Machine (SVM) is used to train the pedestrian classifier. These experiments showed that the proposed FST descriptor has better detection performance over a set of state of the art feature extraction methodologies.

Keywords: Pedestrian Detection, phase congruency, local phase, LBP features, CSLBP features, FST descriptor

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[1] Li Zhang Bo Wu and Ram Nevatia, Pedestrian Detection in Infrared Images Based on Local Shape Features, In CVPR, June 2007.
[2] X. Wang, T. X. Han and S. Yan, An HOG-LBP Human Detector with Partial Occlusion Handling, In ICCV, pp. 32-39, Kyoto, 2009.
[3] Wojek, C., Schiele, B.: A performance evaluation of single and multi-feature people detection. Proceedings of DAGM Symposium on Pattern Recognition'' (2008) 82–91
[4] Oppenheim and J. S. Lim, The importance of phase in signals, Proceedings of the IEEE, vol. 69, no. 5, pp. 529-541, 1981.
[5] P. Kovesi, Image Feature from Phase Congruency, Robotics and Vision Research Group. Technical Report 95/4, March 1995.
[6] P. Kovesi, Phase Congruency Detects Corners and Edges, Proceedings DICTA 2003, Sydney Dec 10-12.
[7] M. Heikkila and C. Schmid, Description of interest regions with local binary patterns, Pattern Recognition., vol. 42, no. 3, pp. 425–436, 2009.
[8] V. Santhaseelan • V. Asari, Utilizing Local Phase Information to Remove Rain from Video, Int J Comput Vis, DOI 10.1007/s11263-014-0759-8, August 2014.
[9] P. Kovesi, Image Feature from Phase Congruency Robotics and Vision Research Group. Technical Report 95/4, March 1995.
[10] C.Yao Su, J. Yang, Histogram of gradient phases: a new local descriptor for face recognition, Published in IET Computer Vision, 2014.
[11] X. Yuan, P. Shi, Iris Feature Extraction Using 2D Phase Congruency, Institute of Image Processing and Pattern Recognition, China, 200030.
[12] Y. Zheng, C. Shen, R. Hartley, X. Huang, Effective Pedestrian Detection Using Center-symmetric Local Binary/Trinary Patterns, IEEE, September 2010
[13] M. Heikkilä, M. Pietikäinen, C. Schmid, Description of Interest Regions with Center-Symmetric Local Binary Patterns, ICVGIP 2006: 58-69.
[14] S. Munder, D. M. Gavrila, An Experimental Study on Pedestrian Classification, IEEE Trans. on Pattern Analysis and Machine Intelligence, 2006.
[15] L. Nanni, S. Brahnam, A. Lumini, A simple method for improving local binary patterns by considering non-uniform patterns, Pattern Recognition 45 (2012) 3844–3852.
[16] N. Dalal and B. Triggs. Histograms of oriented gradients for human detection. In IEEE Conf. Computer Vision and Pattern Recognition (CVPR), volume 1, pages 886–893, 2005.
[17] S. Venkatesh, and R. Owens, (1989). An energy feature detectionscheme. In IEEE International Conference on Image Processing: Conference Proceedings ICIP’89, Sep 5–8 1989, Singapore: IEEE.
[18] M. Morrone and R. Owens. Feature detection from local energy. Pattern Recognition Letters, 6:303–313, 1987.