WEEK 6:DEEP TRACKING

S T U D E N T S :

S I C H E N & M E E RA H A H N

M E N T O R:

A FS H I N D E G H A N

INITIAL EXPERIMENTS ON CNN

C1: feature maps 6@28x28

S1: feature maps 6@14x14

C2: feature maps 12@10x10

S2: f. maps 12@10x10

Convolutions Subsampling SubsamplingConvolutions Fully Connected

• Using the toolbox by Rasmus Berg Palm• Tracking Framework in complete

CAFFEInstallation

Majority of the week

David and Oliver helped us with the installation

Overview

Code with pre-initialized weights from supervised pre-trainingNetwork classifier: 1000 classes --> replaced with an SVMLast layer: 4096 nodes’ feature activation values --> SVM

F SCORE COMPARISONS

Video Names

Autoencoder + SVM

Fully Connected Network

Offline CAFFE Deep

TrackerSTRUCK

Bike 46.09 76.52 96.52 89.47

David 79.13 98.26 98.26 84.52

Deer 98.59 9.86 85.92 97.14

Ironman 18.26 9.57 3.48 3.61

Shaking 70.43 75.65 76.52 37.53

Skiing 49.38 46.91 49.38 6.17

Subway 92.17 26.09 81.74 78.86

Tiger 48.70 18.26 84.35 80.23

Average 62.84 45.14 72.02 59.69

CAFFE

• Trained weights of the CNN on benchmark data set using:

• 256X256 images & 5 convolutional layer network

• 32X32 images & 3 convolution layer network

•95%+ accuracy with trained classifier

• Expectation: larger images trained with more convolutional layers should produce better results

• Next step: Put trained models into tracker

NEXT STEPS• Trained model into our tracker code:

• How well does the tracker preform in comparison to using pre-trained weights?

• Fully connected network

• Learning additional attributes of videos:• Motion: provide temporal data to the network so it

can learn the motion• Scale change