Object tracking using both a kernel and a non-parametric active contour model
| Autor: | Chen De-rong, Yu Hang, Gong Jiu-lu |
|---|---|
| Rok vydání: | 2018 |
| Předmět: |
Active contour model
Level set method Computer science business.industry Cognitive Neuroscience 010401 analytical chemistry Nonparametric statistics Pattern recognition 02 engineering and technology 01 natural sciences 0104 chemical sciences Computer Science Applications Kernel (linear algebra) Kernel (image processing) Artificial Intelligence Computer Science::Computer Vision and Pattern Recognition Video tracking 0202 electrical engineering electronic engineering information engineering Bhattacharyya distance 020201 artificial intelligence & image processing Artificial intelligence Image warping business |
| Zdroj: | Neurocomputing. 295:108-117 |
| ISSN: | 0925-2312 |
| Popis: | By combining both a kernel-based tracking and a non-parametric level set method, a novel framework for target tracking is proposed in this paper that robustly addresses tracking fast-moving and small targets with blurred edges. To establish our new framework, Kullback–Leibler divergence was adopted to measure the divergence between the foreground/background distributions and the target model, and the Bhattacharyya distance was adopted to measure the similarities between the foreground and background distributions. An image warping matrix is introduced into the framework to optimize the target function. The experimental results demonstrate the advantages of the proposed method compared with other methods. |
| Databáze: | OpenAIRE |
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