Proceedings ArticleDOI
Visual tracking via multi-experts combined with average hash model
Yachun Feng,Hong Zhang,Hao Chen,Ding Yuan,Helong Wang +4 more
- pp 331-335
TLDR
This paper proposes a multi-expert selection tracking algorithm that can not only prevent adding bad examples to object model but also can correct the effect of bad updates even if the bad examples are involved.Abstract:
Model-free online object tracking is an important research topic of a wide range of applications in computer vision. A main challenge for object tracking is the model drift problem. In this paper, we proposed a multi-expert selection tracking algorithm that can not only prevent adding bad examples to object model but also can correct the effect of bad updates even if the bad examples are involved. Multi-expert ensemble is constructed of a base tracker and its former snapshots. We choose compressive tracker as our base tracker and introduce an efficient mechanism based on Hash algorithm to prevent bad model updates. Extensive experimental results show that the proposed algorithm performs favorably against state-of-the-art methods. In addition, experiment results on a newly collected dataset with challenging situations demonstrate the better performance of our method.read more
Citations
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Journal ArticleDOI
Towards East Asian Facial Expression Recognition in the Real World: A New Database and Deep Recognition Baseline
Shan Li,Liang Guo,Jianya Liu +2 more
TL;DR: A challenging real-world East Asian facial expression (EAFE) database, which contains 10,000 images collected from 113 Chinese, Japanese, and Korean movies and five search engines, is constructed and the Microsoft Cognitive Face API is used to extract facial attributes in EAFE, so that the database can also be used for facial recognition and attribute analysis.
Journal ArticleDOI
Concealment of iris features based on artificial noises
TL;DR: The experimental results indicate that the security of an iris image can be significantly improved using differential privacy protection, and to hide iris features by differentiating privacy.
References
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TL;DR: Large scale experiments are carried out with various evaluation criteria to identify effective approaches for robust tracking and provide potential future research directions in this field.
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Exploiting the circulant structure of tracking-by-detection with kernels
TL;DR: Using the well-established theory of Circulant matrices, this work provides a link to Fourier analysis that opens up the possibility of extremely fast learning and detection with the Fast Fourier Transform, which can be done in the dual space of kernel machines as fast as with linear classifiers.
Proceedings ArticleDOI
Visual tracking with online Multiple Instance Learning
TL;DR: It is shown that using Multiple Instance Learning (MIL) instead of traditional supervised learning avoids these problems, and can therefore lead to a more robust tracker with fewer parameter tweaks.