摘要
In object tracking algorithms, the target drift and missing are easy to happen in complex environments. A robust tracking algorithm was proposed based on convolutional neural network(CNN) under the particle filter framework. In this algorithm, CNN was utilized to get high-level semantic features of targets and a offline pre-train method was used to learn the general feature representation and improve the training efficiency. Using particle filter framework, the algorithm was arranged to get the reliable target motion state. In addition, two kinds of online updating procedure, long-time and short time, were introduced to deal with the target postures change and some other situations. An online hard example mining strategy was also used to improve the online learning efficiency. The simulation results show that the proposed algorithm can be effectively adapt to complex background, such as occluded, illumination changes, pose variations. Furthermore, we evaluate the proposed algorithm on some challenging videos compared with the state-of-the-art algorithms.
投稿的翻译标题 | Particle Filter for Object Tracking Based on CNN Feature |
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源语言 | 繁体中文 |
页(从-至) | 1256-1262 |
页数 | 7 |
期刊 | Beijing Ligong Daxue Xuebao/Transaction of Beijing Institute of Technology |
卷 | 38 |
期 | 12 |
DOI | |
出版状态 | 已出版 - 1 12月 2018 |
关键词
- Convolutional neural network (CNN)
- Object tracking
- Particle filter