结合PN约束在线半监督boosting目标跟踪算法
文献类型：期刊论文
| 作者 | 李义翠; 亓琳; 谭舒昆 |
| 刊名 | 计算机工程与应用
 |
| 出版日期 | 2017 |
| 关键词 | 目标跟踪 在线学习 半监督学习 目标漂移 结构约束 |
| ISSN号 | 1002-8331 |
| 其他题名 | Object tracking algorithm based on online semi-supervised boosting with structural constraints |
| 产权排序 | 1 |
| 通讯作者 | 李义翠 |
| 中文摘要 | 针对在现有的基于在线半监督boosting的目标跟踪算法中,当目标发生遮挡或快速移动导致分类器更新过程中有错误引入时,其自训练机制会造成分类器错误累积进而产生跟踪漂移甚至导致跟踪失败的问题,提出了一种基于结合正负样本约束的在线半监督boosting的目标跟踪算法(简称PN-SemiT)。该算法在原有的在线半监督boosting跟踪算法的基础上,通过增加正负样本约束条件来实时纠正分类器的错误,并且将目标的先验模型和在线分类器相结合,通过不断迭代更新分类器来预测未标记样本的类别标记和权重。实验结果表明,与传统的在线半监督boosting目标跟踪算法和其它跟踪算法相比,PN-SemiT具有更优异的跟... |
| 英文摘要 | When the tracked objects get seriously obscured or have fast moving, the self-training method based on online semi-supervised boosting will lead to the error accumulation thus easily suffering from the drifting issue or even tracking failure. To overcome the disadvantages, a novel object tracking algorithm is proposed based on online semi-supervised boosting with positive (P) and negative (N) constraints, termed PN-SemiT. The proposed algorithm trains a binary classifier by using online semi-supervised boosting algorithm, and the training process is guided by structural constraints which restrict the labeling of the unlabeled set. Therefore, P-N constraints could evaluate the classifier, identify examples that have been classified in contradiction with structural constraints and adjust the classifier error in real-time. Experimental results on several different challenging video sequences show that the proposed algorithm has a superior tracking performance, and can alleviate the object drifting problem under complex environments. |
| 语种 | 中文 |
| 源URL | [http://ir.sia.cn/handle/173321/19774]  |
| 专题 | 沈阳自动化研究所_光电信息技术研究室 |
| 推荐引用方式 GB/T 7714 | 李义翠,亓琳,谭舒昆. 结合PN约束在线半监督boosting目标跟踪算法[J]. 计算机工程与应用,2017. |
| APA | 李义翠,亓琳,&谭舒昆.(2017).结合PN约束在线半监督boosting目标跟踪算法.计算机工程与应用. |
| MLA | 李义翠,et al."结合PN约束在线半监督boosting目标跟踪算法".计算机工程与应用 (2017). |
入库方式： OAI收割
来源：沈阳自动化研究所
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