Sparse Bayesian Learning for Efficient Visual Tracking O. Williams, A. Blake & R. Cipolloa PAMI, Aug Presented by Yuting Qi Machine Learning Reading Group Duke University 06/24/2005

Overview Motivations - an extension of SVT Bayesian tracking with RVM Overall system Experimental results

Motivations Support vector tracking (SVT) [1] –Training a SVM classifier through the labeled image database; –For a given test image, the tracked object region is located by maximizing the SVM score. –Using first-order Taylor expansion, I final is the linear transformation of image gradient, I x & I y. [1] Shai Avidan, “Support vector tracking”, IEEE Tran. On PAMI, Aug, 2004 [u,v]: motion vector I final : correct object region; I : all possible regions;

Motivations Limitations of SVT –Is the optimization efficient using different kernels? –Is the optimization function suitable? –Smoothing image gradient may decrease performance; Properties of RVM Tracker –Fully probabilistic regression for displacement; –Observations of displacement are fused temporally with motion prediction; –Online tracking;

Bayesian Tracking with RVM Building a displacement expert-RVM –Train an RVM to learn the relationship between images and motion. For a test image region x, RVM returns the displacement : –Mapping from image space to state space. –4 dimensional state space: Translation in x, y, rotation, scaling Each dimension building one RVM

Creating training dataset –Given a seed image I containing the labeled ROI λ; –Generating training examples {z} from I: Sampling random displacements from a uniform distribution: Corresponding state: Generating example z i from state u –Real training examples:

Learning the expert –Given N training examples: {z i, t i }, i=1,…,N. –The relationship between subimages z i and displacement t i is –Considering additive processing noise: –Learning –Posterior is also Gaussian:

Tracking with the expert –Given the test image I, initial state u 0 –Get ROI x by sampling I around u 0. –The expert outputs the probability distribution of the corresponding displacement –Assume the state transition probability is: –Plug those into Kalman-Bucy filter for tracking Gaussian innovation

State predict Innovation State update Tracking algorithm

Overall System A validator is adopted to achieve the tracking robustness. Absence of the verification of the tracked object triggers a exhaustive search over the input image by the classifiers.

Face Tracking Results Row (1): deformation Row (2): occlusion (lost track in the last frame) Row (3): lighting variation (1) (2) (3)

Hand Tracking Results Cars Tracking Results

Long-term Tracking Results

Conclusions Develop a tracker using sparse probabilistic regression by RVMs. RVM can be trained from a single image (generating training set). Robustness is obtained by the object verification.