Research on pedestrian detection based on Semi-Supervised learning

摘要：

In order to implement effective detection and utilize large numbers of unlabeled samples, a pedestrian detection method based on Semi-Supervised learning was presented in this paper. Firstly, BP neural networks classifier, SVM classifier and KNN classifier were selected as the three subclassifiers, and then, the Co-Training mechanism was adopted to train each classifier. Rich information strategy and assistant learning strategy were added in to remove the wrong-marked samples and improve the accuracy of the algorithm by making the most of unlabeled samples. Through the experiments on the test set and real time videos, the feasibility and effectiveness of the approach are verified well.

关键词： pedestrian detection Semi-Supervised learning Co-Training BP neural networks SVM

作者: Zhiwei MA Xiaofeng JIN

作者单位: Intelligent Information Processing Lab., Dept. of Computer Science & Technology, Yanbian University, Yanji 133002, China

会议类型: 国际会议

会议名称: 2012 International Conference on Future Communication and Computer Technology(2012未来通信与计算机技术国际会议ICFCCT 2012)

会议地点: 哈尔滨

会议语种:英文

页码: 362-366

在线出版日期: 2012-05-19（万方平台首次上网日期，不代表论文的发表时间）