A multi-modal moving object detection method based on GrowCut segmentation

Commonly-used motion detection methods, such as background subtraction, optical flow and frame subtraction are all based on the differences between consecutive image frames. There are many difficulties, including similarities between objects and background, shadows, low illumination, thermal halo. Visible light images and thermal images are complementary. Many difficulties in motion detection do not occur simultaneously in visible and thermal images. The proposed multimodal detection method combines the advantages of multi-modal image and GrowCut segmentation, overcomes the difficulties mentioned above and works well in complicated outdoor surveillance environments. Experiments showed our method yields better results than commonly-used fusion methods.

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