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Multi-view Moving Objects Classification via Transfer Learning
Jianyun Liu; Yunhong Wang; Zhaoxiang Zhang; Yi Mo
Conference Name1st Asian Conference on Pattern Recognition
Source PublicationACPR 2011
Conference Date28th November 2011
Conference PlaceBeijing, China
AbstractMoving objects classification in traffic scene videos is a hot topic in recent years. It has significant meaning to intelligent traffic system by classifying moving traffic objects into pedestrians, motor vehicles, non-motor vehicles etc.. Traditional machine learning approaches make the assumption that source scene objects and target scene objects share same distributions, which does not hold for most occasions. Under this circumstance, large amount of manual labeling for target scene data is needed, which is time and labor consuming. In this paper, we introduce TrAdaBoost, a transfer learning algorithm, to bridge the gap between source and target scene. During training procedure, TrAdaBoost makes full use of the source scene data that is most similar to the target scene data so that only small number of labeled target scene data could help improve the performance significantly. The features used for classification are Histogram of Oriented Gradient features of the appearance based instances. The experiment results show the outstanding performance of the transfer learning method comparing with traditional machine learning algorithm.
KeywordFeature Extraction Training Image Edge Detection Training Data Accuracy Videos Support Vector Machines
Document Type会议论文
Corresponding AuthorZhaoxiang Zhang
Recommended Citation
GB/T 7714
Jianyun Liu,Yunhong Wang,Zhaoxiang Zhang,et al. Multi-view Moving Objects Classification via Transfer Learning[C],2011.
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