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Week III: Deep Tracking

Week III: Deep Tracking. Students: Si Chen & Meera Hahn Mentor: Afshin Deghan. STEPS. Current progress. Read: Visual Tracking: an Experimental Survey

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Week III: Deep Tracking

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  1. Week III: Deep Tracking Students: Si Chen & Meera Hahn Mentor: AfshinDeghan

  2. STEPS

  3. Current progress

  4. Read: • Visual Tracking: an Experimental Survey • Arnold W. M. Smeulders*, Senior Member, IEEE, Dung M. Chu*, Student Member, IEEE, Rita Cucchiara, Senior Member, IEEE, Simone Calderara, Senior Member, IEEE, AfshinDehghan, Student Member, IEEE, and Mubarak Shah, Fellow, IEEE • Semi-supervised Learning of Feature Hierarchies for Object Detection in a Video • Yang Yang, GuangShu, Mubarak Shah • Watched: • Recent Developments in Deep Learning & Neural Networks by Geoff Hinton • Unsupervised Feature Learning and Deep Learning by Andrew Ng

  5. Literature review • Convolutional neural networks: • Fully connected layers • Tied weights • Pooling • Fewer parameters, easier to train • Autoencoders: • Feed-forward neural network • Trained with backpropogation: learning outputs from inputs • More inputs = better outputs

  6. Synthetic data generation • Offsetting positive samples 1-5 pixels in each direction • Create different versions by rotating the box Done To Do 90° 270° 180°

  7. Hand crafted VS DEEP FEATURES • Running the same pipeline using different hand-crafted features • Exploring two features: • HOG (In Progress) • Color Histogram (To Be Explored)

  8. Current steps: • HOG code from VLFeat • Ran it on various positive test images from the video sequence Next steps: • Run HOG on the 64 patches from positive test images • Store HOGdescriptors from each patch in a matrix • Run matrices through SVM

  9. Future goals

  10. Exploring different way of generating synthetic data and its effect on feature learning • Comparing the deep features with hand-crafted features • Improving the negative sample collection by incorporating threshold constraint

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