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Boosting Local Feature Descriptors for Automatic Objects Classification in Traffic Scene Surveillance
Zhaoxiang Zhang; Min Li; Kaiqi Huang; Tieniu Tan
2008-12-08
Conference Name19th International Conference on Pattern Recognition
Source Publication Pattern Recognition, 2008. ICPR 2008. 19th International Conference on
Pages1-4
Conference Date8-11 December 2008
Conference PlaceTampa, Florida, USA
AbstractWe 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.
KeywordBoosting Layout Surveillance Videos Object Detection Hidden Markov Models Fuses Noise Robustness Cameras Motion Detection
Language英语
Document Type会议论文
Identifierhttp://ir.ia.ac.cn/handle/173211/12714
Collection智能感知与计算研究中心
Corresponding AuthorZhaoxiang Zhang
Recommended Citation
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.
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