Cross-view object classification in traffic scene surveillance based on transductive transfer learning

Object classification in traffic scene surveillance has been a hot topic in image processing field. A big challenge is that shooting view changes in different scenes, which leads to sharp accuracy decrease since training and test samples do not share the same distribution. Inductive transfer learning methods try to bridge this gap by making use of manually labeled target samples. However, it is in line with reality to conduct unsupervised transfer without manually labeling. In this paper, we propose an intuitive transductive transfer method by transferring instances across view. Experimental results indicate that our method outperforms traditional approaches such as inductive SVM and cluster method, and could even achieve a comparable performance compared with manually labeling approach.