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

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

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

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

Framework MatConvNet Matlab Caffe Python / C++ GPU

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

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

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

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

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

Localization Output the specific points where there is a face

Results

Results

Results

Results

Results

Results