基于外观学习的视频监控实时目标分类

Lun Zhang, S. Li, Xiao-Tong Yuan, Shiming Xiang
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引用次数: 108

摘要

对运动物体进行语义分类是自动视觉监控的重要内容。然而,这是一个具有挑战性的问题,因为与有限的对象尺寸有关的因素,由于不同的视角和照明,同一类中对象的类内变化很大,以及在实际应用中的实时性能要求。本文提出了一种基于外观的方法,在不同摄像机视角下实现实时、鲁棒的目标分类。提出了一种新的描述符,即多块局部二进制模式(MB-LBP),用于捕获物体外观中的大规模结构。基于MB-LBP特征,引入adaBoost算法选择判别特征子集,构造强两类分类器。为了处理MB-LBP特征的非度量特征值,提出了一种多分支回归树作为boosting的弱分类器。最后,引入纠错输出码(ECOC)来实现鲁棒的多类分类性能。实验结果表明,该方法可以在多种场景下实现实时、鲁棒的目标分类。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
Real-time Object Classification in Video Surveillance Based on Appearance Learning
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.
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