1. Mentor: Afshin Dehghan
Deep Tracking
Students: Si Chen & Meera Hahn
Mentor: Afshin Dehghan

2. DT: Pre-Trained Online2
Ran on failure cases = OCDT fscore < 50
Improvement of 12% on failure cases
Update: Improved by 9% over non updated
F - Scores
STRUCK
DT: Pre-Train Offline
DT: Pre-Trained Online
Failure Cases
37.124
22.69
Total
56.28
53.85
62.40

3. Removing Oversampling
Each test image is 256 X 256
-During classification each test image is cropped into 10
227 X 227 images
-The classifier then predicts the scores of the ten cropped images and then takes the average prediction value
-For the failure cases we removed oversampling and got better results
With
Oversampling
Without
Skiing
7.41
23.46
Subway
30.43
77.39
SHOW SKIING VIDEO

5. Merge and Clean up Code
We currently have a couple versions of the code and we are in the process of merging all the code.
Most of the F-scores we have shown you the Deep-tracker which uses the pre-trained network
We have a tracker that trains its own network with fine-tuning. We would like to start working with this code only
Merge code
Implement Scale - Change
Implement Optical flow

6. Optical Flow 1st 4 frames: Simple tracker Track original images
Following frames:
Track optical flow images
Update every 4 frames

7. Optical Flow: New Approach 1
From optical flow vector: vx vy
Vectors into images
Pass in batches of 2
1 Simonyan, Karen and Andrew Zisserman. Two Stream Convolutional Networks for Action Recognition in Videos.