Abstract
People in seats counting is very important for meeting surveillance. While as a canonical pattern recognition problem, it’s very difficult due to various appearances of people and other outliers such as bags and clothes. To solve this problem we propose a coarse-to-fine framework. Firstly, we use the coarse classification module to retrieve the completely empty seats. To overcome the influence of noises caused by shadows and light spots, we fuse multiple global features calculated by background subtraction. Then in the fine classification module, a proposed SW-HOG feature and the LBP feature are combined together to solve the problem of occlusion and make sure the classification is real time. Finally a time-related calibration module is applied to suppress some outliers across frames with condition that the video frames are not successive. Experiments on a real meeting dataset demonstrate that the accuracy of the proposed method reaches 99.88%.
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References
Beymer, D.: Person counting using stereo. In: IEEE Workshop on Human Motion, pp. 127–133 (2000)
Yahiaoui, T., Meurie, C., Khoudour, L., Cabestaing, F.: A People Counting System Based on Dense and Close Stereovision. In: Elmoataz, A., Lezoray, O., Nouboud, F., Mammass, D. (eds.) ICISP 2008 2008. LNCS, vol. 5099, pp. 59–66. Springer, Heidelberg (2008)
Davies, A., Yin, J., Velastin, S.: Crowd monitoring using image processing. Electronics & Communication Engineering Journal 7, 37–47 (1995)
Marana, A., da Fontoura Costa, L., Lotufo, R., Velastin, S.: Estimating crowd density with minkowski fractal dimension. In: IEEE International Conference on Acoustics, Speech, and Signal Processing, vol. 6, pp. 3521–3524 (1999)
Zhao, T., Nevatia, R.: Bayesian human segmentation in crowded situations. In: IEEE Computer Society Conference on Computer Vision and Pattern Recognition 2, pp. II–459 (2003)
Gao, C., Huang, K., Tan, T.: Improvements on mid based foreground segmentation using optical flow. In: 2th China-Japan-Korea Workshop on Pattern Recognition, pp. 154–159 (2010)
Rabaud, V., Belongie, S.: Counting crowded moving objects. In: IEEE Computer Society Conference on Computer Vision and Pattern Recognition, vol. 1, pp. 705–711 (2006)
Shi, J., Tomasi, C.: Good features to track. In: IEEE Computer Society Conference on Computer Vision and Pattern Recognition, pp. 593–600 (1994)
Felzenszwalb, P., Girshick, R., McAllester, D., Ramanan, D.: Object detection with discriminatively trained part-based models. IEEE Transactions on Pattern Analysis and Machine Intelligence 32, 1627–1645 (2010)
Zhang, J., Huang, K., Yu, Y., Tan, T.: Boosted local structured hog-lbp for object localization. In: IEEE Computer Society Conference on Computer Vision and Pattern Recognition, pp. 1393–1400 (2011)
Li, M., Zhang, Z., Huang, K., Tan, T.: Estimating the number of people in crowded scenes by mid based foreground segmentation and head-shoulder detection. In: 19th International Conference on Pattern Recognition, pp. 1–4 (2008)
Dalal, N., Triggs, B.: Histograms of oriented gradients for human detection. In: IEEE Computer Society Conference on Computer Vision and Pattern Recognition, vol. 1, pp. 886–893 (2005)
Ojala, T., Pietikäinen, M., Harwood, D.: A comparative study of texture measures with classification based on featured distributions. Pattern Recognition 29, 51–59 (1996)
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Liang, H., Wu, J., Huang, K. (2012). People in Seats Counting via Seat Detection for Meeting Surveillance. In: Liu, CL., Zhang, C., Wang, L. (eds) Pattern Recognition. CCPR 2012. Communications in Computer and Information Science, vol 321. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-642-33506-8_26
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DOI: https://doi.org/10.1007/978-3-642-33506-8_26
Publisher Name: Springer, Berlin, Heidelberg
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