The Self-Organizing Background Subtraction (SOBS) algorithm implements an approach to moving object detection based on the neural background model automatically generated by a self-organizing method, without prior knowledge about the involved patterns. Such adaptive model can handle scenes containing moving backgrounds, gradual illumination variations and camouflage, can include into the background model shadows cast by moving objects, and achieves robust detection for different types of videos taken with stationary cameras. Moreover, the introduction of spatial coherence into the background update procedure leads to the so-called SC-SOBS algorithm that provides further robustness against false detections. The paper includes extensive experimental results achieved by the SOBS and the SC-SOBS algorithms. The survey lines camera object detection is important one for find the object and its movement. The first step of the object detection is subtract the background in the image. It helps to find the non-moving part in video. There are many methods to use for background subtraction in proposed use Spatially Coherent Self-Organizing Approach of Background Subtraction (SCSOBS) Use the neural network for find the weighted vectors. In that SCSOBS approaches the Tree based coherence method will use to identify the Neighborhood Coherence Factor (NCP). That factors is combine with the SOBS system the background will extract the best combine to the SOBS algorithm.