Week 10 Emily Hand UNR

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

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

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

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

Results

Results

Results

Results

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.