Robust and Fast Collaborative Tracking with Two Stage Sparse Optimization Authors: Baiyang Liu, Lin Yang, Junzhou Huang, Peter Meer, Leiguang Gong and Casimir Kulikowski
Outline Problem of Tracking State of the art algorithms The proposed algorithm Experiment result
The problem Tracking: estimate the state of moving target in the observed video sequences Challenges Illumination, pose of target changes Object occlusion, complex background clutters Landmark ambiguity Two categories of tracking Discriminative Generative
Outline Problem of Tracking State of the art algorithms The proposed algorithm Experiment result
Related work Multiple Instance Learning boosting method(MIL Boosting) put all samples into bags and labeled them with bag labels. Incremental Visual Tracking(IVT) the target is represented as a single online learned appearance model L1 norm optimization a linear combination of the learned template set composed of both target templates and the trivial template.
Basic sparse representation Basis pursuit Disadvantages Computationally expensive Temporal and spatial features are not considered The background pixels do not lie on the linear template subspace
Outline Problem of Tracking State of the art algorithms The proposed algorithm Experiment result
Problem Analysis Given ,Let , , Feature space can be decreased to K0 dimension Two stage greedy method
Stage I: Feature selection Loss function Given , L= as labels, To minimize the loss function, solve the sparse problem below Feature selection matrix
Stage II: Sparse reconstruction Problem after stage I Simplify the aim function above as
Bayesian tracking framework Let represents the affine paramters Estimation of the state probability prediction: updating: Transition model: ~ likelihood where
Review of the algorithm
Outline Problem of Tracking State of the art algorithms The proposed algorithm Experiment result
Visual results
Quantitative results