CASIA OpenIR  > 智能感知与计算研究中心
Boosting Local Feature Descriptors for Automatic Objects Classification in Traffic Scene Surveillance
Zhaoxiang Zhang; Min Li; Kaiqi Huang; Tieniu Tan
2008-12-08
会议名称19th International Conference on Pattern Recognition
会议录名称 Pattern Recognition, 2008. ICPR 2008. 19th International Conference on
页码1-4
会议日期8-11 December 2008
会议地点Tampa, Florida, USA
摘要We address the problem of automatic object classification for traffic scene surveillance, which is very challenging for the low resolution videos, large intra-class variations and real-time requirement. In this paper, we propose a new strategy for object classification by boosting different local feature descriptors in motion blobs. We not only evaluate the performance of each local feature descriptor, but also fuse these descriptors to achieve better performance. Numerous experiments are conducted and experimental results demonstrate the effectiveness and efficiency of our approach with robustness to noise and variance of view angles, lighting conditions and environments.
关键词Boosting Layout Surveillance Videos Object Detection Hidden Markov Models Fuses Noise Robustness Cameras Motion Detection
语种英语
文献类型会议论文
条目标识符http://ir.ia.ac.cn/handle/173211/12714
专题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,Min Li,Kaiqi Huang,et al. Boosting Local Feature Descriptors for Automatic Objects Classification in Traffic Scene Surveillance[C],2008:1-4.
条目包含的文件
条目无相关文件。
个性服务
推荐该条目
保存到收藏夹
查看访问统计
导出为Endnote文件
谷歌学术
谷歌学术中相似的文章
[Zhaoxiang Zhang]的文章
[Min Li]的文章
[Kaiqi Huang]的文章
百度学术
百度学术中相似的文章
[Zhaoxiang Zhang]的文章
[Min Li]的文章
[Kaiqi Huang]的文章
必应学术
必应学术中相似的文章
[Zhaoxiang Zhang]的文章
[Min Li]的文章
[Kaiqi Huang]的文章
相关权益政策
暂无数据
收藏/分享
所有评论 (0)
暂无评论
 

除非特别说明,本系统中所有内容都受版权保护,并保留所有权利。