Week 3 Emily Hand UNR. Online Multiple Instance Learning The goal of MIL is to classify unseen bags, instances, by using the labeled bags as training.

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Presentation transcript:

Week 3 Emily Hand UNR

Online Multiple Instance Learning The goal of MIL is to classify unseen bags, instances, by using the labeled bags as training data. We're going to be working on Online MIL with Guang's template tracking system. The tracker is a correlation-based tracker. Cross correlation is used to find the location of the object being tracked in the next frame.

Reading Visual Tracking with Online Multiple Instance Learning (2009 CVPR) Extracts a bag of potentially positive image patches and picks the best match P-N Learning: Bootstrapping Binary Classifiers by Structural Constraints (2010 CVPR) Identifies examples that have been classified in contradition with given constraints and then adds to the training set, and repeats this process Multiple Instance Boosting for Object Detection (Viola & Platt) Used in P-N Learning

Problems Occlusion is a big problem with the template tracking system. And, sometimes the system gets confused...

Problems (Example)

What I'm Working On Collecting postive and negative samples from the 1st frame of the video Extracting features from the samples Color Histogram Training a classifier (With the above samples) SVM

What I'm Working On MU Detector for video sequences Parsing videos into frames Using the frames as input to the MUDetector VERY slow (at least on my computer) Working on it...

What's Next? Actually training the SVM Detect and update training samples Scanning each frame and getting a score map Choose position with high score This way, if the tracker loses the object, the detector can tell us where it is.