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Published byBrian Thomas Terry Modified over 6 years ago
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Robust and Fast Collaborative Tracking with Two Stage Sparse Optimization
Authors: Baiyang Liu, Lin Yang, Junzhou Huang, Peter Meer, Leiguang Gong and Casimir Kulikowski
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Outline Problem of Tracking State of the art algorithms
The proposed algorithm Experiment result
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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
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Outline Problem of Tracking State of the art algorithms
The proposed algorithm Experiment result
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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.
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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
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Outline Problem of Tracking State of the art algorithms
The proposed algorithm Experiment result
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Problem Analysis Given ,Let , ,
Feature space can be decreased to K0 dimension Two stage greedy method
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Stage I: Feature selection
Loss function Given , L= as labels, To minimize the loss function, solve the sparse problem below Feature selection matrix
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Stage II: Sparse reconstruction
Problem after stage I Simplify the aim function above as
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Bayesian tracking framework
Let represents the affine paramters Estimation of the state probability prediction: updating: Transition model: ~ likelihood where
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Review of the algorithm
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Outline Problem of Tracking State of the art algorithms
The proposed algorithm Experiment result
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Visual results
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Quantitative results
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