1. Week 10
Emily Hand
UNR

2. Online Learning Tracking by detection First Frame Each Following Frame
Manually select target
Extract features from target
Train an SVM
Each Following Frame
Detect target using SVM model
Retrain SVM with old samples and new sample

3. Online Learning Features Sliding Window method for detection
Histogram of Oriented Gradients (HOG)
Local Binary Pattern (LBP)
Both are computed over all 3 color channels
3D Color Histogram
Sliding Window method for detection
Scan the template over a small neighborhood around the previous position of the target.
Test each of these templates against the SVM model

4. Occlusion Handling Partial Occlusion Full Occlusion
Break up template into small blocks.
Determine how these parts contribute to the SVM detection score
Use this information to determine occluded parts of a template
Full Occlusion
Motion Model used for predicted location

5. Occlusion Handling Other methods: Blended Template
Retrain the classifier even if the target is partially occluded
Damages the SVM model
Blended Template
Keep track of all the blocks contributing the most to the SVM score.
Previous Template + Present Template = Blended Template
Retrain Classifier

6. Results

7. Results

8. Results

9. Results

10. Current Work Partial SVM Latent SVM Find parts that are not occluded
Train a new SVM with only those parts and search for that in the next frames
Latent SVM
Each target is made up of 6 parts
All 6 parts have their own SVM
Train on first frame
Search for each individual part
Parts are free to move around.