Partial Block Scheme and Adaptive Update Model for Kernelized Correlation Filters-Based Object Trackingopen access
- Authors
- Jeong, Soowoong; Paik, Joonki
- Issue Date
- Aug-2018
- Publisher
- MDPI
- Keywords
- computer vision; object tacking; correlation filter; partial block; scale space; adaptive learning; discriminative model; partial occlusion; scale variation
- Citation
- APPLIED SCIENCES-BASEL, v.8, no.8
- Journal Title
- APPLIED SCIENCES-BASEL
- Volume
- 8
- Number
- 8
- URI
- https://scholarworks.bwise.kr/cau/handle/2019.sw.cau/1933
- DOI
- 10.3390/app8081349
- ISSN
- 2076-3417
2076-3417
- Abstract
- In visual object tracking, the dynamic environment is a challenging issue. Partial occlusion and scale variation are typical challenging problems. We present a correlation-based object tracking based on the discriminative model. To attenuate the influence by partial occlusion, partial sub-blocks are constructed from the original block, and each of them operates independently. The scale space is employed to deal with scale variation using a feature pyramid. We also present an adaptive update model with a weighting function to calculate the frame-adaptive learning rate. Theoretical analysis and experimental results demonstrate that the proposed method can robustly track drastic deformed objects. The sparse update reduces the computational cost for real-time tracking. Although the partial block scheme generation increases the computational cost, we present a novel sparse update approach to reduce the computational cost drastically for real-time tracking. The experiments were performed on a variety of sequences, and the proposed method exhibited better performance compared with the state-of-the-art trackers.
- Files in This Item
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- Appears in
Collections - Graduate School of Advanced Imaging Sciences, Multimedia and Film > Department of Imaging Science and Arts > 1. Journal Articles
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