The Algorithm Of Moving Objects Detection With Adaptive Visual Background Update

The intelligent video surveillance technology based on the image processing and computer vision has a wide popularization and application.The intelligent video surveillance technology is mainly related to object detection,object tracking,behavior learning,and scene understanding.The object detection is the essential and critical part in video surveillance which directly determines the subsequent operation can be completed successfully.Therefore,how to improve the accuracy of detection and overcome the interference of external conditions is the key factor to realize the moving object detection.The research of moving object detection in video sequences not only has an important theoretical meaning,but also has a high practical value in many fields.In order to improve the accuracy and real-time for the moving object detection,in this thesis,the algorithm of moving objects detection with adaptive visual background update in the video sequences was studied.First,the visual background extraction algorithm based on this thesis presents was to achieve efficient detection.Second,the local sensitivity information of pixel and the local binary similarity patterns descriptor operator were utilized to eliminate the influence which is caused by external conditions.The last,a moving object detection system was designed and implemented.The main work of this thesis was as follows:1.This thesis researched the status of moving objects detection in intelligent video surveillance at home and abroad.There is a summary about existing moving object detection techniques which include these issues of robustness,dynamic background interference,real-time and shadow.2.The visual background extraction algorithm which used the temporal-spatial information of each pixel to replaces the spatial information of original algorithm to initialize to background model.Meanwhile,the 8 neighborhood was replaced by the 24 neighborhood as the selection range of the sample points to decrease the number of wrong sample points in background model.In order to get the segmentation threshold adaptively,this thesis took advantage of the standard deviation of each pixel which reflects the complexity of the background.The improved algorithm could address the ghost at the early detection and improve detection accuracy.3.This article increases the robust to illumination changes by adjusting the local binary similarity patterns descriptor operator.And combined the probability of foreground pixel which determined by the motion entropy with the flickering pixel to realize the dynamic adjustment of segmentation threshold and update factor for each pixel in different environment which decrease the misdtection probability of flickering pixel and improve the accuracy of detection.4.Based on the algorithm of moving objects detection with adaptive visual background update,the moving object detection was designed and implemented.The system has excellent detection effect and low computational complexity,which could meet the requirements of real-time application.