Performance evaluation of infrared moving foreground segmentation without prior knowledge

Chaobo Min, Yingjie Li, and Baohui Zhang


Performance Evaluation, Foreground Segmentation, Image Segmentation


Moving foreground segmentation has been widely used for many infrared video applications. In this work, a metric is presented to evaluate the performance of infrared moving foreground segmentation without prior knowledge. Difference image is given by temporal difference and is quantized into two classes which can differentiate between changed pixel and fixed pixel. In the experiments, we find that, in the segmented image of infrared moving foreground, the difference between the spatial distributions of classes of segmented foreground and background can accurately reflect the difference between segmented foreground and background. So, we can claim if this difference is great, moving foreground segmentation is better. Experiments are performed on variety situation of moving foreground segmentation, showing that the proposed metric is effective.

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