Spatio-temporal motion-based foreground segmentation and shadow suppression

A relevant problem in computer vision is how to detect and track moving objects from video sequences efficiently. Some algorithms require manual calibration in terms of specification of parameters or some hypotheses. A novel method is developed to extract moving objects through multi-scale wavelet transform across background subtraction. The optimal selection of threshold is automatically determined which does not require any complex supervised training or manual calibration. The proposed approach is efficient in detecting moving objects with low contrast against the background and the detection is less affected by the presence of moving objects in the scene. The developed method combines region connectivity with chromatic consistency to overcome the aperture problem. Ghosts are removed by the proposed background update function, which efficiently prevents undesired corruption of background model and does not consider adaptation coefficient. The mentioned approach is scene-independent and the capacity to extract moving object and suppress cast shadow is high. The developed algorithm is flexible and computationally cost-effective. Experiments show that the proposed approach is robust and efficient in segmenting foreground and suppressing shadow by comparison.