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Moving Object Detection Method with Temporal and Spatial Variation Based on Multi-info Fusion

机译:基于多信息融合的时间和空间变化移动对象检测方法

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Aiming at the problem that precision of frame difference for moving object detection is weak, a multi-info fusion model and a novel moving object detection algorithm based on the model are presented in this paper. Firstly, the temporal difference image of two frames in a motion sequence is reconstructed with morphologic operator to obtain target area. Then the spatial-temporal information in the target area is integrated into a fusion image by using the multi- info fusion model. Finally the accurate moving object is detected with automatic threshold segmentation method. The methods of fusion in fusion model are discussed, and a static linear fusion and dynamic self-adaptive fusion based on temporal entropy are presented. The experimental results show that the edge of the obtained moving target with the multi-info fusion method proposed is more accurate than the existing method, and the time complexity is low, which meets the requirement of real-time detection.
机译:针对移动物体检测的帧差的精度较弱的问题,本文介绍了一种多信息融合模型和基于模型的新型移动物体检测算法。首先,将两个帧的运动序列中的两个帧的时间差异图像与形态学操作者重建以获得目标区域。然后使用多信息融合模型将目标区域中的空间信息集成到融合图像中。最后,通过自动阈值分割方法检测到精确的移动物体。讨论了融合模型中的融合方法,并提出了一种基于时间熵的静态线性融合和动态自适应融合。实验结果表明,所获得的移动目标的边缘提出的多信息融合方法比现有方法更准确,时间复杂性低,这符合实时检测的要求。

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