Real-time Object Classification in Video Surveillance Based on Appearance Learning

• Published in: 2007 IEEE Conference on Computer Vision and Pattern Recognition pp. 1 - 8
• Main Authors: Lun Zhang, Li, S.Z., Xiaotong Yuan, Shiming Xiang
• Format: Conference Proceeding
• Language: English; Japanese
• Published: IEEE 01.06.2007
• Subjects: Cameras; Classification tree analysis; Error correction codes; Large-scale systems; Layout; Object recognition; Robustness; Shape; Vehicles; Video surveillance
• ISBN: 9781424411795; 1424411793
• ISSN: 1063-6919; 1063-6919
• DOI: 10.1109/CVPR.2007.383503

Classifying moving objects to semantically meaningful categories is important for automatic visual surveillance. However, this is a challenging problem due to the factors related to the limited object size, large intra-class variations of objects in a same class owing to different viewing angles and lighting, and real-time performance requirement in real-world applications. This paper describes an appearance-based method to achieve real-time and robust objects classification in diverse camera viewing angles. A new descriptor, i.e., the multi-block local binary pattern (MB-LBP), is proposed to capture the large-scale structures in object appearances. Based on MB-LBP features, an adaBoost algorithm is introduced to select a subset of discriminative features as well as construct the strong two-class classifier. To deal with the non-metric feature value of MB-LBP features, a multi-branch regression tree is developed as the weak classifiers of the boosting. Finally, the error correcting output code (ECOC) is introduced to achieve robust multi-class classification performance. Experimental results show that our approach can achieve real-time and robust object classification in diverse scenes.