|Object Classification in Traffic Scene Surveillance Based on Online Semi-Supervised Active Learning|
|Zhaoxiang Zhang; Jie Qin; Yunhong Wang; Meng Liang
|Conference Name||International Conference on Pattern Recognition
|Source Publication||ICPR 2014
|Conference Date||24-28 August 2014
|Conference Place||Stockholm, Sweden
|Abstract||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.|
Support Vector Machines
Image Edge Detection
|Corresponding Author||Zhaoxiang Zhang|
Zhaoxiang Zhang,Jie Qin,Yunhong Wang,et al. Object Classification in Traffic Scene Surveillance Based on Online Semi-Supervised Active Learning[C],2014.
|Files in This Item:||
||There are no files associated with this item.
Items in the repository are protected by copyright, with all rights reserved, unless otherwise indicated.