论文标题
基于本地三个方向模式的强大的行李检测和分类
Robust Baggage Detection and Classification Based on Local Tri-directional Pattern
论文作者
论文摘要
近几十年来，自动视频监视系统在计算机视觉社区中已获得了重要的重视。监视的关键目标是在公共场所监视和安全。在传统的本地二进制图案中，功能描述在某种程度上是不准确的，并且功能大小足够大。因此，为了克服这些缺点，我们的研究提出了一种针对有或没有行李的人类的检测算法。展示了局部三个方向模式描述符，以提取不同人体部位的特征，包括头部，躯干和四肢。然后在支持向量机的帮助下，对提取的功能进行了训练和评估。 InriA和MSMT17 V1数据集的实验结果表明，LTRIDP的表现优于几个最先进的功能描述符，并验证其有效性。
In recent decades, the automatic video surveillance system has gained significant importance in computer vision community. The crucial objective of surveillance is monitoring and security in public places. In the traditional Local Binary Pattern, the feature description is somehow inaccurate, and the feature size is large enough. Therefore, to overcome these shortcomings, our research proposed a detection algorithm for a human with or without carrying baggage. The Local tri-directional pattern descriptor is exhibited to extract features of different human body parts including head, trunk, and limbs. Then with the help of support vector machine, extracted features are trained and evaluated. Experimental results on INRIA and MSMT17 V1 datasets show that LtriDP outperforms several state-of-the-art feature descriptors and validate its effectiveness.
