基于前馈模型和顺序滤波器的异常目标检测

Jiman Kim, Bong-Nam Kang, Hai Wang, Daijin Kim
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引用次数: 7

摘要

异常目标检测与识别是基于视觉的监控系统的一个重要研究领域。本文提出了一种新的异常物体(如遗弃和被盗物体)的检测和识别算法。该算法由三个阶段和三个不同的滤波器组成。这三个阶段通过前馈模型相互配合,提高了检测和识别性能,而顺序滤波器则利用三类信息有效地剔除了错误检测的区域。利用公共数据集进行的实验结果表明,与现有系统相比,该算法具有更高的准确率和更低的虚警率。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
Abnormal Object Detection Using Feedforward Model and Sequential Filters
Abnormal object detection and discrimisnation is a critical research area for vision-based surveillance systems. This paper proposes a novel algorithm for the detection and discrimination of abnormal objects, such as abandoned and stolen objects. The proposed algorithm consists of three stages and three different filters. The three stages cooperate with each other using the feedforward model to enhance detection and discrimination performance, while the sequential filters efficiently reject falsely detected regions using three categories of information. The results of experiments conducted using public datasets indicate that the proposed algorithm is more accurate and has a lower false alarm ratio than the existing system.
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