Counting People using Gradient Boosted Trees

摘要：

　　This paper proposes a real-time approach to count the people in crowded scenes using holistic properties of the video,without using individual detection or tracking.A group of efficient holistic features are firstly extracted from the crowd segmentations.Furthermore,a nonlinear function mapping the feature vector into the number of people is learned with gradient boosted trees.Experiments demonstrate that our method can perform more accurate people counting.

关键词： gradient boosting crowd analysis surveillance

作者: Bingyin Zhou Ming Lu Yonggang Wang

作者单位: College of Mathematics and Information Sciences Hebei Normal University Shijiazhuang 050024,China

会议类型: 国际会议

会议名称: 2016IEEE第二届信息技术、网络、电子及自动化控制会议

会议地点: 重庆

会议语种:英文

页码: 391-395

在线出版日期: 2016-03-20（万方平台首次上网日期，不代表论文的发表时间）