1. Presenter: Usman Sajid
CNN-based Cascaded Multi-task Learning of High-level Prior and Density Estimation for Crowd Counting (Vishwanath A. Sindagi, Vishal M. Patel, Rutgers University)
Presenter: Usman Sajid

2. Why this Paper?
Among top 3 results in 2017 (In top 5 in different datasets till date as cited by several EECV, CVPR 2018 papers)
Research implementation aligns with some part of this paper (may reuse the code)
Used their code (model) to prove my hypothesis
Simple yet effective approach

3. Crowd Analysis (Counting)
Many applications
Political rallies, Public Ceremonies
Hajj
One of problems: non-uniform large variations in scale and appearance of the objects
Previously, models do not focus much on different count distribution within an image

4. Crowd Analysis (Counting)
No one model giving best results in 3 mostly use datasets (UCF_CC_50, Shanghai_Tech (A & B), UCG_QNRF (recently released))

5. Proposed Model
novel end-to-end cascaded network of CNNs to jointly learn crowd count classification and density map estimation

6. Proposed Model 2 stages: High Level Prior and Density Estimator
Empirical Observation till date: Better to use density maps rather than direct regression
But very poor in localization

7. Proposed Model- High Level Prior
10 way crowd count classifier
Classification or regression???

8. Proposed Model- Density Estimator
Final stage, resulting in density map

9. Objective Function 2 losses: one for each stage
For High Level Prior: Cross Entropy loss
for the density estimation:

10. Training Details Very few training images available (e.g. 300 images)
Create Additional Images
patches of size 1/4 th the size of original image are cropped from 100 random locations
Augmentation techniques like horizontal flipping and noise addition are used to create another 200 patches
So total 300 patches of arbitrary sizes from each image are extracted
NVIDIA GTX TITAN-X GPU using Torch framework
Training for 6 hours

11. Evaluation Criteria 2 Criteria widely used in this particular field
MAE and MSE as follows:

12. Datasets for Crowd Counting
Number of  Images
Number of  Annotations
Average Count
Maximum Count
Average Resolution
UCF_CC_50 50
63,974
1279
4633
2101 x 2888
ShanghaiTech_PartA
482
241,677
501
3139
589 x 868
UCF-QNRF
Came Approx. 3 weeks back
1535
1,251,642
815
12865
2013 x 2902

13. Results- Shanghai Tech

14. Results- Shanghai Tech
Current Best (as reported in CVPR 18):
Part A, MAE: 68.2, MSE: Approx
Part B, MAE: Approx. 10.6, MSE: Approx. 16.0

15. Results - UCF
Current Best:
MAE: 266.1, MSE: Approx

16. Conclusion
Proposed multi-task cascaded CNN network for jointly learning crowd count classification and density map estimation
Incorporated a high level prior into the network which enables it to learn globally relevant discriminative features thereby accounting for large count variations in the dataset
End-to-End trained
Top 3 Best in 2017 (Still in top 5)

17. Thank you 