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. ---------------------------------- 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 --------------------------------- Find me on: Blog (Arabic) https://aiage-ar.blogspot.com.eg/ (English) https://aiage.blogspot.com.eg/ YouTube https://www.youtube.com/AhmedGadFCIT Google Plus https://plus.google.com/u/0/+AhmedGadIT SlideShare https://www.slideshare.net/AhmedGadFCIT LinkedIn https://www.linkedin.com/in/ahmedfgad reddit https://www.reddit.com/user/AhmedGadFCIT ResearchGate https://www.researchgate.net/profile/Ahmed_Gad13 Academia https://menofia.academia.edu/Gad Google Scholar https://scholar.google.com.eg/citations?user=r07tjocAAAAJ&hl=en Mendelay https://www.mendeley.com/profiles/ahmed-gad12 ORCID https://orcid.org/0000-0003-1978-8574 StackOverFlow http://stackoverflow.com/users/5426539/ahmed-gad Twitter https://twitter.com/ahmedfgad Facebook https://www.facebook.com/ahmed.f.gadd Pinterest https://www.pinterest.com/ahmedfgad

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. Index
• Introduction
• Challenges
• Perspective Distortion
• Non-Linearity
• Proposed Method
• Experimental Results
20 December 2017 1

3. Problem Definition
Crowd Counting – Crowd Density Estimation
CountEstimation
Counting
Regression20 December 2017
Introduction Challenges Proposed Method Experimental Results
2

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. 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. 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. 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. Non-Linearity
Region Pixels and People Count Relationship
20 December 2017
Introduction Challenges Proposed Method Experimental Results
7

9. Proposed Method
20 December 2017
Introduction Challenges Proposed Method Experimental Results
8

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. Proposed Feature Vector Proposed
Feature
Vector
• Region
• GLCM
• GLGCM
• HOG
• LBP
• SIFT
• Edge Strength
20 December 2017 10

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. 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. UCSD Glitches
20 December 2017
Core i7 – 16 GB
RAM – scikit learn
Introduction Challenges Proposed Method Experimental Results
13

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. Comparison with Previous Works
20 December 2017
Introduction Challenges Proposed Method Experimental Results
15

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. Cross Validation
Wise Training & Testing Samples Selection
20 December 2017
Introduction Challenges Proposed Method Experimental Results
17

19. Partial Features Training & Testing
MSE
20 December 2017
Introduction Challenges Proposed Method Experimental Results
18

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. 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. 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