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Object Classification in Traffic Scene Surveillance Based on Online Semi-Supervised Active Learning
Zhaoxiang Zhang; Jie Qin; Yunhong Wang; Meng Liang
2014-08-24
Conference NameInternational Conference on Pattern Recognition
Source PublicationICPR 2014
Conference Date24-28 August 2014
Conference PlaceStockholm, Sweden
AbstractObject 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.
KeywordAccuracy Joints Surveillance Training Semisupervised Learning Support Vector Machines Image Edge Detection
Document Type会议论文
Identifierhttp://ir.ia.ac.cn/handle/173211/13238
Collection类脑智能研究中心
Corresponding AuthorZhaoxiang Zhang
Recommended Citation
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],2014.
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