Sparse Bayesian Learning for Efficient Visual Tracking

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

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

3. 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, Ifinal is the linear transformation of image gradient, Ix & Iy. Ifinal: correct object region; I: all possible regions; [u,v]: motion vector [1] Shai Avidan, “Support vector tracking”, IEEE Tran. On PAMI, Aug, 2004

4. 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;

5. 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

6. 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 zi from state u • Real training examples:

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

8. Tracking with the expert • Given the test image I, initial state u0 • Get ROI x by sampling I around u0. • 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

9. Tracking algorithm State predict Innovation State update

10. 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.

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

12. Hand Tracking Results Cars Tracking Results

13. Long-term Tracking Results

14. 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.