Transferring Training Instances for Convenient Cross-View Object Classification in Surveillance
Zhaoxiang Zhang, Yuhang Zhao, Yunhong Wang, Jianyun Liu, Zhenjun Yao, Jun Tang · IEEE Transactions on Information Forensics and Security · 2013
Automatic object classification is an important issue in traffic scene surveillance. Appearance variation due to perspective distortion is one of the most difficult problems for moving object detection, tracking, and recognition. We propose an active transfer learning approach to bridge the gap between appearance variations under two different scenes. Only a small number of training samples are required in the target scene, which can be combined with transferred samples of the source scene to achieve a reliable object classifier in the target scene, and active learning strategy makes the algorithm more efficient. Abundant experiments are conducted and experimental results demonstrate the effectiveness and convenience of our approach.