1. Counting in Dense Crowds using Deep Learning
Logan Lebanoff
Mentor: Haroon Idrees
Counting in Dense Crowds using Deep Learning

2. Ground truth=1567, Proposed=1590
Previous Work
Ground truth=1567, Proposed=1590

3. Applications Concerts Political Speeches Rallies Marathons Stadiums
Crowd management
Safety/surveillance

4. Fully-Connected Layer
Our Approach
Deep Learning
Convolutional Neural Networks (CNNs)
Image
Convolutional Layers
Fully-Connected Layer
Output Layer

5. Framework
MatConvNet
Matlab
Caffe
Python / C++
GPU

6. Pretrained models
Instead of training the network from scratch, we fine-tune an existing model
AlexNet
8 weight layers
ImageNet-vgg-verydeep-19
19 weight layers

7. Training 50 crowd images Split into image patches
Resize patches to 227x227x3 pixels
Input to network
Various convolutional layers, max pooling layers, and fully connected layers

8. Loss function
Experimented on different loss functions for the last layer
Classification problem
Regression problem

9. Classification Network output a vector Softmax loss
Size of vector is the max number of people in a patch
For each index i, the value of the vector represents the confidence score that there are i people present in the image patch
Softmax loss

10. Regression Network output a single number Experiments
Represent the expected number of people in the image patch
Experiments
Euclidean Loss
Sum of squares
of differences
Other

11. Localization
Output the specific points where there is a face

12. Results

13. Results

14. Results

15. Results

16. Results

17. Results