Object Classification in Traffic Scene Surveillance Based on Online Semi-Supervised Active Learning
文献类型：会议论文
| 作者 | Zhaoxiang Zhang ; Jie Qin; Yunhong Wang; Meng Liang
|
| 出版日期 | 2014-08-24 |
| 会议日期 | 24-28 August 2014 |
| 会议地点 | Stockholm, Sweden |
| 关键词 | Accuracy Joints Surveillance Training Semisupervised Learning Support Vector Machines Image Edge Detection |
| 英文摘要 | Object Classification in traffic scene surveillance has gained popularity in recent years. Traditional methods tend to utilize a large number of labeled training samples to achieve a satisfactory classification performance. However, labels of samples are not always available and manual labeling work is both time and labor consuming. To address the problem, a large number of semi-supervised learning based methods have been proposed, but most of them only focus on the offline settings. Motivated by an active learning framework, a novel online learning strategy is proposed in this paper. Furthermore, an intuitive semi-supervised learning method, which incorporates the spirits of both the online and active learning, is proposed and utilized in the scenario of traffic scene surveillance. The proposed learning framework is evaluated on the BUAA-IRIP traffic database, and the observed superior performance proves the effectiveness of our approach. |
| 会议录 | ICPR 2014
 |
| 源URL | [http://ir.ia.ac.cn/handle/173211/13238]  |
| 专题 | 自动化研究所_智能感知与计算研究中心 |
| 通讯作者 | Zhaoxiang Zhang |
| 推荐引用方式 GB/T 7714 | Zhaoxiang Zhang,Jie Qin,Yunhong Wang,et al. Object Classification in Traffic Scene Surveillance Based on Online Semi-Supervised Active Learning[C]. 见:. Stockholm, Sweden. 24-28 August 2014. |
入库方式： OAI收割
来源：自动化研究所
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