ABSTRACT A novel approach for object categorization suitable for video surveillance is proposed. We describe shapes only using radius and arclength of their curvatures, which allow differentiating between objects that appear in the monitored area. We conducted experiments on classes such as pedestrians, cars, cyclists, and animals (horse, cow, dog, and cat). Our approach achieves a reasonable accuracy () on Kimia dataset, surpasses the accuracy of the state-of-the-art methods () on CDnet videos, and allows handling cases of object merge and split usually present in foreground masks issued from background subtraction of videos.

中文翻译：

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快速二元形状分类

摘要提出了一种适用于视频监控的对象分类新方法。我们仅使用曲率的半径和弧长来描述形状，这允许区分出现在监控区域中的对象。我们对行人、汽车、骑自行车的人和动物（马、牛、狗和猫）等类别进行了实验。我们的方法在 Kimia 数据集上实现了合理的准确度 ()，超过了 CDnet 视频上最先进方法 () 的准确度，并允许处理通常存在于从背景减法发出的前景蒙版中的对象合并和拆分情况视频。