Abstract
In order to avoid crowd disaster in public gatherings, this paper aims to develop an efficient algorithm that works well in both indoor and outdoor scenes to give early warning message automatically. It also deals with high dense crowd and sudden illumination changing environment. To address this problem, first an XCS-LBP (eXtended Center Symmetric Local Binary Pattern) features are extracted which works well under sudden illumination changes. Subsequently, these features are trained using deep Convolutional Neural Network (CNN) for crowd count. Finally, a warning message is displayed to the authority, if the people count exceeds a certain limit in order to avoid the crowd disaster in advance. Benchmark datasets such as PETS2009, UCSD and UFC_CC_50 have been used for experimentation. The performance measures such as MSE (Mean Square Error), MESA (Maximum Excess over Sub Arrays) and MAE (Mean Absolute Error) have been calculated and the proposed approach provides high accuracy.
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References
Silva, C., Bouwmans, T., Frelicot, C.: An eXtended Center-Symmetric Local Binary Pattern for Background Modeling and Subtraction in Video. In: 10th International Joint Conference on Computer Vision, Imaging and Computer Graphics Theory and Applications (VISAPP), pp. 1–8. (2015)
Sami Abdulla, Mohsen Saleh, Shahrel Azmin Suandi, Haidi Ibrahim.: Recent survey on crowd density estimation and counting for visual surveillance. In: Engineering Applications of Artificial Intelligence, vol. 41, pp. 103–114. Pergamon Press, Inc. Tarrytown, NY, USA (2015)
Rahmalan, H., Nixon, M.S., Carter, J.N.: On crowd density estimation for surveillance. In: The Institution of Engineering and Technology Conference on Crime and Security, pp. 540–545. IET, London (2006)
Pratik P. Parate, Mandar Sohani.: Crowd Density Estimation Using Local Binary Pattern Based on an Image. In International Journal of Advanced Research in Computer Science and Software Engineering, vol. 5, Issue 7, pp. (2015)
Zhe Wang, Hong Liu, Yueliang Qian, Tao Xu.: Crowd Density Estimation Based On Local Binary Pattern Co-Occurrence Matrix. In: IEEE International Conference on Multimedia and Expo Workshops (ICMEW), pp. 372– 377. IEEE, Melbourne, VIC (2012)
Ojala, T., Pietikainen, M., Maenpaa, T.: Multi resolution gray-scale and rotation invariant texture classification with local binary patterns. In: IEEE Transactions on Pattern Analysis and Machine Intelligence, vol. 24, Issue 7, pp. 971–987. IEEE (2002)
Marana, A.N., da Fontoura Costa, L., Lotufo, R.A., Velastin, S.A.: Estimating crowd density with minkowski fractal dimension. In: IEEE International Conference on Acoustics, Speech and Signal Processing, vol. 6, pp. 3521–3524. IEEE, Phoenix, AZ (1999)
Wenhua Ma, Lei Huang, Changping Liu: Advanced Local Binary Pattern Descriptors for Crowd Estimation. In: Pacific-Asia Workshop on Computational Intelligence and Indus trial Application, PACIIA’08, pp. 958–962. IEEE, Wuhan (2008)
Heikkila Marko, Pietikainen Matti, Schmid Cordelia: Description of Interest Regions with Center-Symmetric Local Binary Patterns. In: 5th Indian Conference on Computer Vision, Graphics and Image Processing, ICVGIP, vol. 4338, pp. 58–69. (2006)
Cong Zhang, Hongsheng Li, Xiaogang Wang, Xiaokang Yang: Cross-Scene Crowd Counting via Deep Convolutional Neural Networks. In: IEEE Conference on Computer Vision and Pattern Recognition (CVPR), pp. 833–841. IEEE, Boston, MA (2015)
Carlos Arteta, Victor Lempitsky, Alison Noble, J., Andrew Zisserman: Interactive Object Counting. In: 13th European Conference Computer Vision ECCV, vol. 8691, pp. 504–518. Springer (2014)
An, S., Liu, W., Venkatesh, S.: Face Recognition Using Kernel Ridge Regression. In: IEEE Conference on Computer Vision and Pattern Recognition CVPR’07, pp. 1–7. IEEE, Minneapolis, MN (2013)
Chen, K., Loy, C.C., Gong, S., Xiang, T.: Feature mining for localized crowd counting. In: Proceedings of British Machine Vision Conference, BMVC (2012)
Acknowledgments
This work has been supported under DST Fast Track Young Scientist Scheme for the project entitled, Intelligent Video Surveillance System for Crowd Density Estimation and Human Abnormal Analysis, with reference no. SR/FTP/ETA-49/2012.
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Nagananthini, C., Yogameena, B. (2017). Crowd Disaster Avoidance System (CDAS) by Deep Learning Using eXtended Center Symmetric Local Binary Pattern (XCS-LBP) Texture Features. In: Raman, B., Kumar, S., Roy, P., Sen, D. (eds) Proceedings of International Conference on Computer Vision and Image Processing. Advances in Intelligent Systems and Computing, vol 459. Springer, Singapore. https://doi.org/10.1007/978-981-10-2104-6_44
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DOI: https://doi.org/10.1007/978-981-10-2104-6_44
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