Research on Target Detection of Regional Monitoring with Complex Background using CNN and Background Modelling

Weichen Sun, ZhanHua Yang, Bo Zhao, Y. Wang, Zhonglin Yang, Yutong Jiang, Haiping Song
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Abstract

The regional monitoring systems aim to recognize and localize the target of interest in the region area. However, the target detection algorithm currently used in the regional monitoring system has problems such as low recognition probability under complex background conditions. The type of moving object recognized by the background modelling algorithm is difficult to judge. This paper proposes a monitoring area target detection method that fuses the detection results of the two YOLOv5 target detection algorithms and the Vibe background modelling method through Kalman filtering. Experiments show that the proposed method can improve the consistency and stability of target detection results in regional monitoring scenarios.
基于CNN和背景建模的复杂背景下区域监测目标检测研究
区域监测系统的目的是识别和定位区域内的目标。然而,目前区域监测系统中使用的目标检测算法在复杂背景条件下存在识别概率低的问题。背景建模算法识别的运动目标类型难以判断。本文提出了一种通过卡尔曼滤波融合两种YOLOv5目标检测算法和Vibe背景建模方法检测结果的监测区域目标检测方法。实验表明,该方法可以提高区域监测场景下目标检测结果的一致性和稳定性。
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