Paper
6 May 2019 Combining discriminant embedding and transfer learning for visual tracking
Jieyan Liu, Ao Ma, Mengmeng Jing
Author Affiliations +
Proceedings Volume 11069, Tenth International Conference on Graphics and Image Processing (ICGIP 2018); 1106946 (2019) https://doi.org/10.1117/12.2524364
Event: Tenth International Conference on Graphic and Image Processing (ICGIP 2018), 2018, Chengdu, China
Abstract
Visual tracking can be viewed as a discrimination problem to distinguish the target object from the background. However, it is difficult to get efficient training samples and there is usually a strong similarity between the foreground and the background in reality, which makes it challenging to discriminate the target object from the background. In this paper, we present a tracking method based on the combination of discriminant embedding and transfer learning to tackle the challenge. For one thing, we use the graph embedding method to characterize the relationship between the foreground samples and the background samples, for another we exploit the knowledge of the tracking results in the previous frame to track the next frame. We then learn a subspace by jointly optimizing discriminant embedding and transfer learning into a unified framework. The classifier is constructed on the learned subspace to discriminate the target from the background. Experiments on several video benchmarks demonstrate the effectiveness of our approach.
© (2019) COPYRIGHT Society of Photo-Optical Instrumentation Engineers (SPIE). Downloading of the abstract is permitted for personal use only.
Jieyan Liu, Ao Ma, and Mengmeng Jing "Combining discriminant embedding and transfer learning for visual tracking", Proc. SPIE 11069, Tenth International Conference on Graphics and Image Processing (ICGIP 2018), 1106946 (6 May 2019); https://doi.org/10.1117/12.2524364
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KEYWORDS
Particles

Optical tracking

Video

Data modeling

Detection and tracking algorithms

Electronic filtering

Matrix multiplication

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