目的 针对多运动目标在移动背景情况下跟踪性能下降和准确度不高的问题,本文提出了一种基于OPTICS聚类与目标区域概率模型的方法。方法 首先引入了Harris-Sift特征点检测,完成相邻帧特征点匹配,提高了特征点跟踪精度和鲁棒性;再根据各运动目标与背景运动向量不同这一点,引入了改进后的OPTICS加注算法,在构建的光流图上聚类,从而准确的分离出背景,得到各运动目标的估计区域;对每个运动目标建立一个独立的目标区域概率模型(OPM),随着检测帧数的迭代更新,以得到运动目标的准确区域。结果 多运动目标在移动背景情况下跟踪性能下降和准确度不高的问题通过本文方法得到了很好地解决,Harris-Sift特征点提取、匹配时间仅为Sift特征的17%。在室外复杂环境下,本文方法的平均准确率比传统背景补偿方法高出14%,本文方法能从移动背景中准确分离出运动目标。结论 实验结果表明,该算法能满足实时要求,能够准确分离出运动目标区域和背景区域,且对相机运动、旋转,场景亮度变化等影响因素具有较强的鲁棒性。

Objective 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. Method 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. Result 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. Conclusion 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.