1. WEEK 6: DEEP TRACKING STUDENTS: SI CHEN & MEERA HAHN MENTOR: AFSHIN DEGHAN

2. 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 Convolutions Fully Connected Using the toolbox by Rasmus Berg Palm Tracking Framework in complete

3. CAFFE Installation Majority of the week David and Oliver helped us with the installation Overview Code with pre-initialized weights from supervised pre-training Network classifier: 1000 classes --> replaced with an SVM Last layer: 4096 nodes’ feature activation values --> SVM

4. F SCORE COMPARISONS Video Names Autoencoder + SVM Fully Connected Network Offline CAFFE Deep Tracker STRUCK Bike 46.0976.5296.5289.47 David 79.1398.26 84.52 Deer 98.599.8685.9297.14 Ironman 18.269.573.483.61 Shaking 70.4375.6576.5237.53 Skiing 49.3846.9149.386.17 Subway 92.1726.0981.7478.86 Tiger 48.7018.2684.3580.23 Average62.8445.1472.0259.69

5. 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

6. 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