1. Convolutional Neural Networks for Visual Tracking
Computer Vision Lab.
남현섭

2. Contents Convolutional Neural Networks Tracking by CNN
J. Fan, et al., Human tracking using convolutional neural networks, Neural Networks, IEEE Transactions on, 2010
H. Li, et al., DeepTrack: Learning Discriminative Feature Representations by Convolutional Neural Networks for Visual Tracking, BMVC, 2014
On-going research

3. Convolutional Neural Network

4. J. Fan, et al., Human tracking using convolutional neural networks, Neural Networks, IEEE Transactions on, 2010

5. Contributions
Learn both spatial and temporal features from image pairs of two adjacent images.
Use multiple path ways in CNN to fuse local and global information.
Use Shift-variant CNN architecture to alleviate the drift problem to distracting objects.

6. CNN Architecture

7. Shift-Variant Architecture
Shift-invariant
Shift-variant

8. Handling Scale Change

9. Results temporal&spatial features spatial features only
global&local branch, shift-variant
global branch only
local branch only
Shift-invariant

10. Results

11. Results

12. H. Li, et al., DeepTrack: Learning Discriminative Feature Representations by Convolutional Neural Networks for Visual Tracking, BMVC, 2014

13. Contributions A candidate pool of multiple CNNs
=> temporal adaptation
Structural loss function
=> large, reliable training examples
Class-specific tracking
=> Combine class-level detector and instance-level tracker

14. CNN Architecture

15. Structural Loss Function
Traditional loss function
Structural loss function
Structural importance
CNN loss
overlapping ratio
=> Can use the training samples with high importance to avoid class ambiguity.

16. Online Learning: A Coordinate-Descent
=> Reduce overfitting, increase training speed

17. Temporal Adaptation With a CNN Pool

18. Temporal Adaptation With a CNN Pool
Can accommodate as many as possible appearance variations without learning an ensemble of CNNs of a very complicated CNN
Can explicitly refine the model pool and discard unreliable CNNs

19. Class-Specific Tracking
Combine the class-level detector and the instance-level tracker

20. Results

21. Results

22. Results – Class Specific Tracking

23. Observations Need to combine low-level and high-level information.
Deep CNN features lack of exact localization ability.
Learning a CNN with few examples leads an overfitting problem.

24. On-Going Research
Learning a CNN
Probability map
Re-initialize