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WEEK 6: DEEP TRACKING

WEEK 6: DEEP TRACKING. Students: Si Chen & Meera Hahn Mentor: Afshin Deghan. Initial Experiments on CNN. Subsampling. Fully Connected. Convolutions. Subsampling. Convolutions. Using the toolbox by Rasmus Berg Palm Tracking Framework in complete. C2: feature maps 12@10x10.

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WEEK 6: DEEP TRACKING

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  1. WEEK 6:DEEP TRACKING Students: Si Chen & Meera Hahn Mentor: AfshinDeghan

  2. Initial Experiments on CNN Subsampling Fully Connected Convolutions Subsampling Convolutions • Using the toolbox by Rasmus Berg Palm • Tracking Framework in complete C2: feature maps 12@10x10 S2: f. maps 12@10x10 S1: feature maps 6@14x14 C1: feature maps 6@28x28

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

  4. F Score Comparisons

  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

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