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Visual Tracking via Auto-Encoder Pair Correlation Filter
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AI
DOI：10.1109/TIE.2019.2913815.png)
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Abstract
En 中文
Robust visual tracking is one of the most challenging problems in computer vision applications. However, the limited training data and the computational complexity have severely affected tracking performance. In this paper, we propose an auto-encoder pair model for visual tracking which is composed of source domain network and target domain network to help a more accurate localization. We adopt the dense circular samples of the object state to increase the number of training samples and prevent model overfitting. Meanwhile, a difference regularization term is also introduced into our framework to penalize the large appearance variations of the object in two domains. The alternating optimization is used to solve the optimization problems. Furthermore, our method alleviates the model update problem and improves the tracking speed by using long-term and short-term updating scheme. In addition, the target domain filter is updated by introducing the updated source domain filter to avoid the object drift. Comprehensive experiments on some challenging benchmarks demonstrate that our approach concurrently improves both tracking accuracy and speed.
Keywords:
Visualization
Correlation
Target tracking
Training data
Deep learning
Training
Decoding
Auto-encoder network
correlation filter (CF)
optimization
visual tracking
Journal
IF:
7.2
Papers:
1.8W
Citations:
9.8W
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