Crowd Density Estimation Using Multiple Feature
Categories and Multiple Regression ModelsPresented By
Ahmed F. Gad
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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Proposed Method
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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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UCSD Crowd Counting Dataset
4,000 Image20,000 Region
Plenty of Data
Pedestrian LocationLabeled Regions
Strong GT
1220 December 2017
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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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Comparison with Previous Works
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Unbalanced Training & Testing Sets
Without CVJust 35 level
With CVAll Levels
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Cross ValidationWise Training & Testing Samples Selection
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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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