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

INITIAL EXPERIMENTS ON CNN C1: feature maps S1: feature maps C2: feature maps S2: f. maps Convolutions Subsampling Convolutions Fully Connected Using the toolbox by Rasmus Berg Palm Tracking Framework in complete

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

F SCORE COMPARISONS Video Names Autoencoder + SVM Fully Connected Network Offline CAFFE Deep Tracker STRUCK Bike David Deer Ironman Shaking Skiing Subway Tiger Average

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