icces 2017 - crowd density estimation method using regression analysis

Crowd Density Estimation Using Multiple Feature

Categories and Multiple Regression ModelsPresented By

Ahmed F. Gad

ahmed.fawzy@ci.menofia.edu.eg

Menoufia UniversityFaculty of Computers and InformationInformation Technology Department

Co-AuthorsAssoc. 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

Index

• Introduction

• Challenges• Perspective Distortion• Non-Linearity

• Proposed Method

• Experimental Results

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Problem DefinitionCrowd Counting – Crowd Density Estimation

CountEstimation

Counting

Regression20 December 2017

Introduction Challenges Proposed Method Experimental Results

2

Crowd Counting ApproachesDetection-Based Crowd Counting

Holistic Partial

Test

Classifier

OcclusionOvercrowded

Scenes

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Introduction Challenges Proposed Method Experimental Results

3

Crowd Counting ApproachesRegression

• Solves the requirements to detect and track objects.

• Counting based on groups not individuals.

• Depends on qualitative measures from the ability of humansto count people in crowded scenes.

Scene Analysis Features

Count

X

Y20 December 2017

Introduction Challenges Proposed Method Experimental Results

4

Perspective DistortionWhy 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

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Introduction Challenges Proposed Method Experimental Results

5

Perspective Normalization

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Introduction Challenges Proposed Method Experimental Results

6

Zhang, Li, et al. "Crowd density estimation based on convolutional neural networks with mixed pooling." Journal of ElectronicImaging 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).

Non-LinearityRegion Pixels and People Count Relationship

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Introduction Challenges Proposed Method Experimental Results

7

Proposed Method

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Introduction Challenges Proposed Method Experimental Results

8

Features per Segmented Region

Image Foreground Region

Working locally per segmented regions allows capturing variance between each two regions.

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Introduction Challenges Proposed Method Experimental Results

9

Proposed Feature Vector Proposed

Feature

Vector

• Region

• GLCM

• GLGCM

• HOG

• LBP

• SIFT

• Edge Strength

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Regression Modelling

Features CountRegression Model

Independent Dependent

GPR

RF

RPF

LASSO

KNN

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Introduction Challenges Proposed Method Experimental Results

11

UCSD Crowd Counting Dataset

4,000 Image20,000 Region

Plenty of Data

Pedestrian LocationLabeled Regions

Strong GT

1220 December 2017

Introduction Challenges Proposed Method Experimental Results

UCSD Glitches

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Core i7 – 16 GB RAM – scikit learn

Introduction Challenges Proposed Method Experimental Results

13

ResultsTraining 5 regression models with all features

Evaluation Metrics: MSE, MAE, and MRE

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Introduction Challenges Proposed Method Experimental Results

14

Comparison with Previous Works

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Introduction Challenges Proposed Method Experimental Results

15

Unbalanced Training & Testing Sets

Without CVJust 35 level

With CVAll Levels

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Introduction Challenges Proposed Method Experimental Results

16

Cross ValidationWise Training & Testing Samples Selection

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Introduction Challenges Proposed Method Experimental Results

17

Partial Features Training & TestingMSE

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Conclusion

• New crowd density estimation method based on multiplefeatures and multiple regression models.

• Edge strength is a newly used features in crowd densityestimation.

• 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).

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References

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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 edgeletbased 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.

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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.

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References

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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 onApplications 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," arXivpreprint 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.

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