Moving object detection and tracking is an important step for many computer vision applications. Considering the presence of background movement and target motion

moving object detection and tracking in dynamic background became more complex. Thus

such detection and tracking is one of the important

difficult problems in machine vision research. This article proposed a method based on the OPTICS clustering algorithm and the model of probability area

aiming to increase target-tracking performance and accuracy of a moving camera. This article combined the advantages of SIFT feature description and Harris corner detection

aiming to solve the problem of large computation of SIFT algorithm and large error of object area. First

the Harris-Sift feature points detection are introduced. The feature points are not only the significant corner points

but also possess scale invariant features. After establishing the key points of feature description

adjacent feature point is matched by Best Bin First algorithm

improving tracing accuracy and robustness of feature point. To analyze the motion of the feature point better

the motion vector of the feature points is converted into the corresponding optical flow coordinate

adopting gird

and data binning technique to determine the scope of the search range of data points. Based on to the difference in moving target and background motion vector

the improved OPTICS algorithm is introduced to cluster on the grid structure

which could significantly reduce computation time of the algorithm. After obtaining the estimation of the moving object area

this article defined a class of feature points that are the most widely distributed in the image as the background

whereas other feature points represent a moving object. After separating the background and moving objects to track the target continuously

the tracking strategy is to search for the upper frame moving target area's feature points in the next frame. Although the OPTICS algorithm removed most of the noise

there are still errors between the real region of the objects and the estimation of the region. Next

based on each moving target

an independent probability model is built. This article establishes an independent object probability model based on continuous video frames for each moving object

along with the iteration of testing area

the real aiming area is then extracted. In this article

a new method is proposed to solve the problem of low accuracy and tracking performance of multiple moving objects tracking in a complex environment. The experimental results demonstrate that the proposed algorithm

which is based on the premise of meeting real-time

can extract the multiple moving objects from the complex moving background accurately. Experimental results show that the proposed Harris-Sift feature point detection algorithm can greatly reduce calculation time. Thus

the algorithm can meet real-time requirements. The proposed method can accurately separate each moving object from the background not only indoors but also under complex outdoor environments;it can also adapt to changing illumination and camera movement.