|Boosting Local Feature Descriptors for Automatic Objects Classification in Traffic Scene Surveillance|
|Zhaoxiang Zhang; Min Li; Kaiqi Huang; Tieniu Tan
|Conference Name||19th International Conference on Pattern Recognition
|Source Publication|| Pattern Recognition, 2008. ICPR 2008. 19th International Conference on
|Conference Date||8-11 December 2008
|Conference Place||Tampa, Florida, USA
|Abstract||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.|
Hidden Markov Models
|Corresponding Author||Zhaoxiang Zhang|
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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