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ICCES 2017 - Crowd Density Estimation Method using Regression Analysis

The oral presentation of the paper titled "Crowd Density Estimation Method using Multiple Feature Categories and Multiple Regression Models".
This paper was accepted for publication and oral presentation in the 12th IEEE International Conference on Computer Engineering and Systems (ICCES 2017) held from 19 to 20 December 2017 in Cairo, Egypt.
The paper proposed a new method to estimate the number of people within crowded scenes using regression analysis. The two challenges in crowd density estimation using regression analysis are perspective distortion and non-linearity. This paper solves the perspective distortion using perspective normalization which is the best way to deal with that problem based on recent works.
The second challenge is solved by creating a new combination of features collected from multiple already existing categories including segmented region, texture, edge, and keypoints. This paper created a feature vector of length 164.
Five regression models are used which are GPR, RF, RPF, LASSO, and KNN.
Based on the experimental results, our proposed method gives better results than previous works.
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أحمد فوزي جاد Ahmed Fawzy Gad
قسم تكنولوجيا المعلومات Information Technology (IT) Department
كلية الحاسبات والمعلومات Faculty of Computers and Information (FCI)
جامعة المنوفية, مصر Menoufia University, Egypt
Teaching Assistant/Demonstrator
ahmed.fawzy@ci.menofia.edu.eg
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ICCES 2017 - Crowd Density Estimation Method using Regression Analysis

  1. 1. Crowd Density Estimation Using Multiple Feature Categories and Multiple Regression Models Presented By Ahmed F. Gad ahmed.fawzy@ci.menofia.edu.eg Menoufia University Faculty of Computers and Information Information Technology Department Co-Authors Assoc. Prof. Khalid M. Amin Dr. Ahmed M. Hamad 20 December 2017 PID 107 12th IEEE International Conference on Computer Engineering and Systems (ICCES 2017), Cairo, Egypt
  2. 2. Index • Introduction • Challenges • Perspective Distortion • Non-Linearity • Proposed Method • Experimental Results 20 December 2017 1
  3. 3. Problem Definition Crowd Counting – Crowd Density Estimation CountEstimation Counting Regression20 December 2017 Introduction Challenges Proposed Method Experimental Results 2
  4. 4. Crowd Counting Approaches Detection-Based Crowd Counting Holistic Partial Test Classifier Occlusion Overcrowded Scenes 20 December 2017 Introduction Challenges Proposed Method Experimental Results 3
  5. 5. Crowd Counting Approaches Regression • Solves the requirements to detect and track objects. • Counting based on groups not individuals. • Depends on qualitative measures from the ability of humans to count people in crowded scenes. Scene Analysis Features Count X Y 20 December 2017 Introduction Challenges Proposed Method Experimental Results 4
  6. 6. Perspective Distortion Why Perspective Distortion is a Problem? • Crowd counting in regression uses pixel count to find the people count in a region. • Due to perspective distortion, the same areas with the same size can have different people count. P, X P 20 December 2017 Introduction Challenges Proposed Method Experimental Results 5
  7. 7. Perspective Normalization 20 December 2017 Introduction Challenges Proposed Method Experimental Results 6 Zhang, Li, et al. "Crowd density estimation based on convolutional neural networks with mixed pooling." Journal of Electronic Imaging 26.5 (2017): 051403-051403. Xu, Xiaohang, Dongming Zhang, and Hong Zheng. "Crowd Density Estimation of Scenic Spots Based on Multifeature Ensemble Learning." Journal of Electrical and Computer Engineering 2017 (2017).
  8. 8. Non-Linearity Region Pixels and People Count Relationship 20 December 2017 Introduction Challenges Proposed Method Experimental Results 7
  9. 9. Proposed Method 20 December 2017 Introduction Challenges Proposed Method Experimental Results 8
  10. 10. Features per Segmented Region Image Foreground Region Working locally per segmented regions allows capturing variance between each two regions. 20 December 2017 Introduction Challenges Proposed Method Experimental Results 9
  11. 11. Proposed Feature Vector Proposed Feature Vector • Region • GLCM • GLGCM • HOG • LBP • SIFT • Edge Strength 20 December 2017 10
  12. 12. Regression Modelling Features Count Regression Model Independent Dependent GPR RF RPF LASSO KNN 20 December 2017 Introduction Challenges Proposed Method Experimental Results 11
  13. 13. UCSD Crowd Counting Dataset 4,000 Image 20,000 Region Plenty of Data Pedestrian Location Labeled Regions Strong GT 1220 December 2017 Introduction Challenges Proposed Method Experimental Results
  14. 14. UCSD Glitches 20 December 2017 Core i7 – 16 GB RAM – scikit learn Introduction Challenges Proposed Method Experimental Results 13
  15. 15. Results Training 5 regression models with all features Evaluation Metrics: MSE, MAE, and MRE 20 December 2017 Introduction Challenges Proposed Method Experimental Results 14
  16. 16. Comparison with Previous Works 20 December 2017 Introduction Challenges Proposed Method Experimental Results 15
  17. 17. Unbalanced Training & Testing Sets Without CV Just 35 level With CV All Levels 20 December 2017 Introduction Challenges Proposed Method Experimental Results 16
  18. 18. Cross Validation Wise Training & Testing Samples Selection 20 December 2017 Introduction Challenges Proposed Method Experimental Results 17
  19. 19. Partial Features Training & Testing MSE 20 December 2017 Introduction Challenges Proposed Method Experimental Results 18
  20. 20. Conclusion • New crowd density estimation method based on multiple features and multiple regression models. • Edge strength is a newly used features in crowd density estimation. • Three experiments conducted: 1. Less error compared to recent works using all features. 2. Enhanced results using cross validation. 3. Ranking features based on their accuracy in prediction. (Edge strength, SIFT, and LBP are the best). 20 December 2017 19
  21. 21. References 1. C. C. Loy, K. Chen, S. Gong, and T. Xiang, "Crowd counting and profiling: Methodology and evaluation," Modeling, Simulation and Visual Analysis of Crowds,Springer, pp. 347-382, 2013. 2. W. Zhen, L. Mao, and Z. Yuan, "Analysis of trample disaster and a case study–Mihong bridge fatality in China in 2004," Safety Science, vol. 46, pp. 1255-1270, 2008. 3. D. Helbing, A. Johansson, and H. Z. Al-Abideen, "Dynamics of crowd disasters: An empirical study," Physical review E, vol. 75, p. 046109, 2007. 4. B. Krausz and C. Bauckhage, "Loveparade 2010: Automatic video analysis of a crowd disaster," Computer Vision and Image Understanding, vol. 116, pp. 307-319, 2012. 5. B. Wu and R. Nevatia, "Detection and tracking of multiple, partially occluded humans by bayesian combination of edgelet based part detectors," International Journal of Computer Vision, vol. 75, pp. 247-266, 2007. 6. D. Ryan, S. Denman, S. Sridharan, and C. Fookes, "An evaluation of crowd counting methods, features and regression models," Computer Vision and Image Understanding, vol. 130, pp. 1-17, 2015. 7. A. B. Chan, Z.-S. J. Liang, and N. Vasconcelos, "Privacy preserving crowd monitoring: Counting people without people models or tracking,". IEEE Conference on Computer Vision and Pattern Recognition (CVPR), pp. 1-7, 2008. 8. A. B. Chan and N. Vasconcelos, "Counting people with low-level features and Bayesian regression," IEEE Transactions on Image Processing, vol. 21, pp. 2160-2177, 2012. 9. L. Dong, V. Parameswaran, V. Ramesh, and I. Zoghlami, "Fast crowd segmentation using shape indexing,". IEEE 11th International Conference on Computer Vision (ICCV), pp. 1-8, 2007. 10. Z. Q. Al-Zaydi, D. L. Ndzi, M. L. Kamarudin, A. Zakaria, and A. Y. Shakaff, "A robust multimedia surveillance system for people counting," Multimedia Tools and Applications, pp. 1-28, 2016. 20 December 2017 20
  22. 22. References 11. R. Liang, Y. Zhu, and H. Wang, "Counting crowd flow based on feature points," Neurocomputing, vol. 133, pp. 377-384, 2014. 12. D. G. Lowe, "Distinctive image features from scale-invariant keypoints," International journal of computer vision, vol. 60, pp. 91-110, 2004. 13. K. Chen, C. C. Loy, S. Gong, and T. Xiang, "Feature Mining for Localised Crowd Counting," BMVC, p. 3, 2012. 14. B. Xu and G. Qiu, "Crowd density estimation based on rich features and random projection forest,"IEEE Winter Conference on Applications of Computer Vision (WACV), pp. 1-8, 2016. 15. D. Kong, D. Gray, and H. Tao, "A viewpoint invariant approach for crowd counting," 18th International Conference on in Pattern Recognition (ICPR). pp. 1187-1190, 2006. 16. Zeng, Xinchuan, and Tony R. Martinez. "Distributed-balanced stratified cross-validation for accuracy estimation." Journal of Experimental & Theoretical Artificial Intelligence vol. 12, pp. 1-12, 2000. 17. Ojala, Timo, Matti Pietikainen, and Topi Maenpaa. "Multiresolution gray-scale and rotation invariant texture classification with local binary patterns." IEEE Transactions on pattern analysis and machine intelligence, vol. 24, pp. 971-987, 2002. 18. S. L. Kukreja, J. Löfberg, and M. J. Brenner, "A least absolute shrinkage and selection operator (LASSO) for nonlinear system identification," IFAC Proceedings Volumes, vol. 39, pp. 814-819, 2006. 19. D. Kang, D. Dhar, and A. B. Chan, "Crowd Counting by Adapting Convolutional Neural Networks with Side Information," arXiv preprint arXiv:1611.06748, 2016. 20. C. Zhang, H. Li, X. Wang, and X. Yang, "Cross-scene crowd counting via deep convolutional neural networks," IEEE Conference on Computer Vision and Pattern Recognition, pp. 833-841, 2015. 20 December 2017 21

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