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

2. Research Goals
Use autoencoders and convolutional neural networks to learn the best features for tracking
Learning features and classifiers together (currently using SVM classifer)

3. Tracker Implementation
STEPS
Introduction
Getting familiar with current code
Reading applicable literature
Getting familiarized with deep learning concepts
Tracker Implementation
Running tracker with different features
HOG
Color Histogram
Challenges
Handing changes in scale of target
Updating the model

4. Current progress

5. Visual Tracking: an Experimental Survey
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, Afshin Dehghan, Student Member, IEEE, and Mubarak Shah, Fellow, IEEE
Semi-supervised Learning of Feature Hierarchies for Object Detection in a Video
Yang Yang, Guang Shu, Mubarak Shah
Watched:
Recent Developments in Deep Learning & Neural Networks by Geoff Hinton
Unsupervised Feature Learning and Deep Learning by Andrew Ng

6. 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

7. 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°

8. 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)

9. 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 HOG descriptors from each patch in a matrix
Run matrices through SVM

10. Future goals

11. Comparing the deep features with hand-crafted features
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
Rotation invariant features
----- Meeting Notes (5/30/14 12:45) -----
new models for collecting positve and negative test samples
already comparing autoencoders and HOG, go further
go to next slide