Boosting local feature descriptors for automatic objects classification in traffic scene surveillance

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 fe...

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Bibliographic Details
Main Authors: Zhaoxiang Zhang, Min Li, Kaiqi Huang, Tieniu Tan
Format: Conference Proceeding
Language:English
Subjects:
Boosting
Cameras
Fuses
Hidden Markov models
Layout
Motion detection
Noise robustness
Object detection
Surveillance
Videos
Online Access:Request full text
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Summary: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.
ISSN:1051-4651
2831-7475
DOI:10.1109/ICPR.2008.4761317