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Reduction of False Positives in Head-Shoulder Detection Based on Multi-Part Color Segmentation

Authors: Lae-Jeong Park


The paper presents a method that utilizes figure-ground color segmentation to extract effective global feature in terms of false positive reduction in the head-shoulder detection. Conventional detectors that rely on local features such as HOG due to real-time operation suffer from false positives. Color cue in an input image provides salient information on a global characteristic which is necessary to alleviate the false positives of the local feature based detectors. An effective approach that uses figure-ground color segmentation has been presented in an effort to reduce the false positives in object detection. In this paper, an extended version of the approach is presented that adopts separate multipart foregrounds instead of a single prior foreground and performs the figure-ground color segmentation with each of the foregrounds. The multipart foregrounds include the parts of the head-shoulder shape and additional auxiliary foregrounds being optimized by a search algorithm. A classifier is constructed with the feature that consists of a set of the multiple resulting segmentations. Experimental results show that the presented method can discriminate more false positive than the single prior shape-based classifier as well as detectors with the local features. The improvement is possible because the presented approach can reduce the false positives that have the same colors in the head and shoulder foregrounds.

Keywords: Feature Extraction, Pedestrian Detection, color segmentation, false positives

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[1] M. Enzweiler and D. M. Gavrila, "Monocular pedestrian detection: Survey and experiments," IEEE Trans. on Pattern Analysis and Machine Intelligence, vol. 31, pp. 2179–2195, 2009.
[2] K-Y K. Wong, R. H. Y. Chung, Y. L. Francis, and K-P P. Chow, "Real-time multiple head shape detection and tracking system with decentralized trackers," in Proc. Intelligent Systems Design and Application, 2006, pp. 384–389.
[3] B. Wu and R. Nevatia, "Detection and tracking of multiple, partially occluded humans by Bayesian combination of edgelet part detectors," Int. J. of Computer Visions, vol. 75, pp. 247–266, 2007.
[4] M. Li, Z. Zhang, K. Huang, and T. Tan, "Estimating the number of people in crowded scenes by MID based foreground segmentation and head-shoulder detection," in Proc. Int. Conf. on Pattern Recognition, 2008, pp. 1–4.
[5] X. Ding, H. Xu, P. Cui, L. Sun, and S. Yang, "A cascade SVM approach for head-shoulder detection using histograms of oriented gradients," in Proc. IEEE Int. Symposium on Circuits and Systems, 2009, pp. 1791–1794.
[6] C. Zeng and H. Ma, "Robust head-shoulder detection by PCA-based multilevel HOG-LBP detector for people counting," in Proc. Conf. on Pattern Recognition, 2010, pp. 2069–2072.
[7] L.-J. Park and J-H Moon, “Exploiting global self-similarity for head-shoulder detection,” World Academy of Science, Engineering, and Technology, vol. 7, no. 4, pp. 144–148, 2013.
[8] N. Dalal and B. Triggs, "Histograms of oriented gradients for human detection," in Proc. IEEE Conf. on Computer Vision and Pattern Recognition, 2005, pp. 886–893.
[9] D. G. Lowe, “Object recognition from local scale-invariant features,” in Proc. IEEE Conf. on Computer Vision, 1999, pp. 1150–1157.
[10] P. Dollar, Z.Tu, P. Perona, and S. Belongie, "Integral channel features," in Proc. British Machine Vision Conference, 2009,
[11] S. Walk, N. Majer, K. Schindler, and B. Schiele, "New features and insights for pedestrian detection," in Proc. IEEE Conf. on Computer Vision and Pattern Recognition, 2010, pp. 1030–1037.
[12] S. K. Diwala, D. Hoiem, J. H. Hays, A. A. Efros, and M. Hebert, “An empirical study of context in object detection,” in Proc. IEEE Conf. on Computer Vision and Pattern Recognition, 2009, pp. 1271–1278.
[13] P. Ott and M Everingham, "Implicit color segmentation features for pedestrian and object detection," in Proc. IEEE Conf. on Computer Vision, 2009, pp. 723–730.
[14] D. Ramanan, "Using segmentation to verify object hypothesis," in Proc. IEEE Conf. on Computer Vision and Pattern Recognition, 2007, pp. 1–8.
[15] L.-J. Park, “Exploiting color segmentation in pedestrian upper-body detection,” J. of the institute of Electronics and Information Engineers, vol. 51, no. 11, pp. 181–186, 2014 (in Korean).
[16] Y. Boykov, O. Veksler, and R. Zabih, “Fast approximate energy minimization via graph cuts,” in Proc. IEEE Conf. on Computer Vision, 1999, pp. 374–384.
[17] S. Kirkpatrick, C. D. Gelatt Jr., and M. P. Vecchi, “Optimization by simulated annealing,” Science, vol. 220, pp. 671–680, 1983.