Fast and robust object tracking using tracking failure detection in kernelized correlation filteropen access
- Authors
- Shin J.; Kim H.; Kim D.; Paik, Joonki
- Issue Date
- Jan-2020
- Publisher
- MDPI AG
- Keywords
- Correlation filter; Multi-domain convolutional neural network; MDNet; Real-time tracking; Visual tracking
- Citation
- Applied Sciences (Switzerland), v.10, no.2
- Journal Title
- Applied Sciences (Switzerland)
- Volume
- 10
- Number
- 2
- URI
- https://scholarworks.bwise.kr/cau/handle/2019.sw.cau/38599
- DOI
- 10.3390/app10020713
- ISSN
- 2076-3417
2076-3417
- Abstract
- Object tracking has long been an active research topic in image processing and computer vision fields with various application areas. For practical applications, the object tracking technique should be not only accurate but also fast in a real-time streaming condition. Recently, deep feature-based trackers have been proposed to achieve a higher accuracy, but those are not suitable for real-time tracking because of an extremely slow processing speed. The slow speed is a major factor to degrade tracking accuracy under a real-time streaming condition since the processing delay forces skipping frames. To increase the tracking accuracy with preserving the processing speed, this paper presents an improved kernelized correlation filter (KCF)-based tracking method that integrates three functional modules: (i) tracking failure detection, (ii) re-tracking using multiple search windows, and (iii) motion vector analysis to decide a preferred search window. Under a real-time streaming condition, the proposed method yields better results than the original KCF in the sense of tracking accuracy, and when a target has a very large movement, the proposed method outperforms a deep learning-based tracker, such as multi-domain convolutional neural network (MDNet). © 2020 by the authors.
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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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