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Foreground Segmentation for Moving Cameras under Low Illumination Conditions

A foreground segmentation method, including image enhancement, trajectory classification and object segmentation, 
 
is proposed for moving cameras under low illumination conditions. Gradient-field-based image 
 
enhancement is designed to enhance low-contrast images. On the basis of the dense point trajectories obtained 
 
in long frames sequences, a simple and effective clustering algorithm is designed to classify foreground 
 
and background trajectories. By combining trajectory points and a marker-controlled watershed algorithm, 
 
a new type of foreground labeling algorithm is proposed to effectively reduce computing costs and improve 
 
edge-preserving performance. Experimental results demonstrate the promising performance of the proposed 
 
approach compared with other competing methods.