Multi-feature united sparse represented target tracking method
A joint sparse and target tracking technology, applied in the field of target tracking of multi-feature joint sparse representation, can solve problems such as high environmental requirements, tracking failure, and narrow adaptation range.
- Summary
- Abstract
- Description
- Claims
- Application Information
AI Technical Summary
Problems solved by technology
Method used
Image
Examples
Embodiment 1
[0078] The multi-feature joint sparseness in step 7 in the first embodiment is based on the sparse representation of the original feature vector. However, in many vision problems, feature descriptions (vectors) are often encoded in the form of kernel matrices. In order to combine multiple kernel features, we extend the original feature space to RKHS to revisit the sparse representation problem.
[0079] The kernel function technique consists in: for each feature k, use a non-linear function φ k Map the dictionary template and candidate samples from the original feature space to another high-dimensional RKHS, in this high-dimensional space, for some given kernel function g k , with φ k (x i ) T φ k (x j ) = g k (x i ,x j ). In the new space, we rewrite the formula (1) in Step 7 of Embodiment 1 as:
[0080] min W 1 2 Σ k = 1 ...
PUM
Abstract
Description
Claims
Application Information
- R&D Engineer
- R&D Manager
- IP Professional
- Industry Leading Data Capabilities
- Powerful AI technology
- Patent DNA Extraction
Browse by: Latest US Patents, China's latest patents, Technical Efficacy Thesaurus, Application Domain, Technology Topic, Popular Technical Reports.
© 2024 PatSnap. All rights reserved.Legal|Privacy policy|Modern Slavery Act Transparency Statement|Sitemap|About US| Contact US: help@patsnap.com