计算机科学
人工智能
计算机视觉
跟踪(教育)
编码器
滤波器(信号处理)
眼动
模式识别(心理学)
BitTorrent跟踪器
算法
卡尔曼滤波器
视频跟踪
相关性
自编码
作者
Xu Cheng,Yifeng Zhang,Lin Zhou,Yuhui Zheng
出处
期刊:IEEE Transactions on Industrial Electronics
[Institute of Electrical and Electronics Engineers]
日期:2020-04-01
卷期号:67 (4): 3288-3297
被引量:6
标识
DOI:10.1109/tie.2019.2913815
摘要
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.
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