Application Research And Realization On Real-time Detection And Tracking Technology Of Moving Object

Moving objects detection and tracking is one of the most important issues in applied vision and moving image coding and has wide applications in the fields of Virtual Reality and Visual Surveillance. Object detection in videos involves verifying the presence of an object in image sequences and possibly locating it precisely. Object tracking is to monitor an object's spatial and temporal changes during a video sequence, including its presence, position, size, shape, etc. These two processes are closely related because tracking usually starts with detecting objects, while detecting an object repeatedly in subsequent image sequence is often necessary to verify tracking.Based on studying and comparing several methods used in moving object detection in videos sequences and combined the experiments in this thesis,it is desided to make use of method based on Backgroud differencing with Multimode Backgrond Modeling to extract moving foreground and self-adatped meanwhile dynamical updated characteristics to reflect the changes in background timely.However,it remain to contain the uninteresting objects in background and leads to the concurrence of interesting and objects,that is to say,this kind of method can not remove the objects which we do not focus on. In consequence tracking result will be greatly affected in later stage.So this thesis posts an improved method which is based on the GMM Backgrond modeling of Backgroud differencing called Moving object filtering that ground on characteristics.When no intense light changes or slight shake happened, it can be more effectively to eliminate the most part of inaccuracy judge or detection obtained from the Multimode method in the self-adaptive process of studying moving object characteristics.This step is crucial premise to processing in later step in that the alarming error rate of uninteresting objects detection will be reduced and this is also important to save countless human and financial resources under the construction of conservation-minded society and persuit of socioeconomic performance.Due to getting the dependable order of accuracy result of moving object detection in former stage,the advantage of MeanShift combined with Kalman Filtering algorithm is used with result of foreground detection added as the primary input of Kalman Filtering and this will further increase the accuracy of object tracking meanwhile this method is better in dealing with hidden object among a small circle.All of these preparation work provides dependable information and foundation to the judgement of tracking in later stage. In orde to take full advantage of the obtained trace, this thesis designs and implements an Offset-Tracking Alarming System based on Offset Threshold trough learning the mode of moving object adaptively on Windows platform.Regardless of whether it is indoor or outdoor under the condition that there is slight movement of background or this movement is not obvious, this system can provide a customizable parameter choice therefore the program flexibility is greatly increased.At last, the system is testing many times under various experiment conditions and parameters and results are analyzed comprehensively and it is verified that the method based on characteristics filtering this thesis posted can filter uninteresting moving objects in vedio timely and meanwhile save the interesting objects. Consequently, alarming error rate is greatly decreased contrarily alarming precision and robustness is increased.