目的 基于目标模型匹配方法被广泛用于运动物体的检测与跟踪。针对传统模型匹配跟踪方法易受局部遮挡、复杂背景等因素影响的问题，提出一种前景划分下的双向寻优BOTFP （Bidirectional optimization tracking method under foreground partition）跟踪方法。方法 首先，在首帧中人工圈定目标区域，提取目标区域的颜色、纹理特征，建立判别外观模型。然后，利用双向最优相似匹配方法进行目标检测，计算测试图像中的局部特征块与建立的外观模型之间的相似性，从而完成模型匹配过程。为了避免复杂背景和相似物干扰，提出一种前景划分方法约束匹配过程，得到更准确的匹配结果。最后，提出一种在线模型更新算法，引入了距离决策，判断是否发生误匹配，避免前景区域中相似物体的干扰，保证模型对目标的描述更加准确。结果 本文算法与多种优秀的跟踪方法相比，可以达到相同甚至更高的跟踪精度，在Girl、Deer、Football、Lemming、Woman、Bolt、David1、David2、Singer1以及Basketball视频序列下的平均中心误差分别为7.43、14.72、8.17、13.61、24.35、7.89、11.27、13.44、12.18、7.79，跟踪重叠率分别为0.69、0.58、0.71、0.85、0.58、0.78、0.75、0.60、0.74、0.69。与同类方法L1APG （L1 tracker using accelerated proximal gradient approach），TLD （tracking-learning-detection），LOT （local orderless tracker）比较，平均跟踪重叠率提升了20%左右。结论 实验结果表明，在前景区域中，利用目标的颜色特征和纹理特征进行双向最有相似匹配，使得本文算法在部分遮挡、目标形变、复杂背景、目标旋转等条件下具有跟踪准确、适应性强的特点。

Objective Target tracking plays an important role in computer vision and is widely used in intelligent traffic,robot vision,and motion capture.In actual scenes,the accuracy of target tracking is low because of the influence of illumination change,target deformation,partial occlusion,and complex background.Thus,avoiding the influence of these factors and improving the tracking accuracy of the algorithm are major issues in target tracking.Target model matching methods are widely used in the detection and tracking of moving targets.In recent years,many experts and scholars have proposed several excellent target model matching tracking methods.Babenko et al.proposed a target tracking method based on online multiple instance learning,and this method selects the appropriate number of positive and negative templates around the target to track.The authors also constructed a discriminant model to achieve tracking and updated the appearance model of the target in real time.Wang et al.proposed a superpixel tracking method,which extracts the target model from the background and forms a dividing target model.The authors also calculated the possible position of the target at the subsequent moment by use of the maximum a posteriori estimation and the pixel confidence map.Mei et al.proposed a tracking method based on sparse representation classification,and this method is used to solve the problem of sparse approximation.The authors also determined the final tracking result on the basis of the size of the reconstructed residuals.Bao et al.proposed a real-time sparse representation tracking method,which uses multiple target models and sparse representation classification.This method can improve the tracking effectiveness while maintaining a high tracking accuracy.Kalal et al.proposed a tracking method based on the combination of tracking,learning,and detection.This method is robust to the local occlusion and the target deformation.Oron et al.proposed a locally orderless tracking,which divides the target into a plurality of superpixel blocks and tracks the targets in the subsequent frames by matching the pixels.At the same time,the authors used a particle filter to restrain target model matching and thus ensure robust tracking.Traditional model matching and tracking methods are easily affected by the local occlusion of other targets and the complex background.Thus,a novel tracking approach based on the bidirectional optimization tracking method under foreground partition (BOTFP) is proposed to solve these problems.Method In the first frame during manual delineation of the target area,the color and texture features of the target region are extracted and used to establish the discriminant appearance model.Subsequently,the similarity between the local features of the test images and the appearance models is calculated using the bidirectional optimization similarity matching method to complete the model matching process.This study presents a foreground partition method,which can obtain accurate matching results,to avoid the interference of complex background and similar targets.Finally,an online model updating algorithm is proposed,which introduces the distance decision method.This algorithm can be used to determine whether a false match occurs,avoid the interference of similar targets in the foreground region,and ensure that the model is an accurate description of the target.Result Compared with that of other excellent tracking algorithms,the proposed target tracking algorithm can achieve the same or even higher tracking accuracy.The average center errors in video sequences of Girl,Deer,Football,Lemming,Woman,Bolt,David1,David2,Singer1,and Basketball are 7.43,14.72,8.17,13.61,24.35,7.89,11.27,13.44,12.18,and 7.79,respectively.The tracking overlap ratios in video sequences of Girl,Deer,Football,Lemming,Woman,Bolt,David1,David2,Singer1,and Basketball are 0.69,0.58,0.71,0.85,0.58,0.78,0.75,0.60,0.74,and 0.69,respectively.The average running speeds (frame/s) in video sequences of Girl,Deer,Football,Lemming,Woman,Bolt,David1,David2,Singer1,and Basketball are 8.14,7.32,7.78,6.69,6.31,7.57,6.73,7.17,5.97,and 6.38,respectively.Compared with that of similar methods (e.g.,L1APG,TLD,and LOT),the average tracking overlap rate of the proposed method is higher by approximately 20%.Conclusion Experimental results indicate that the use of the color and texture features of the target in conducting bidirectional optimization similarity matching of the foreground region ensures accurate tracking and strong adaptability of the algorithm under the conditions of partial occlusion,deformation,and complex background.The characteristics of BOTFP are as follows:1) A perfect appearance model is obtained when using the color and texture features of the target in conducting bidirectional optimization similarity matching of the foreground region.2) The foreground information of the image frames is estimated and evaluated,and the matching process is restricted to avoid the interference of background information.3) The result is robust when using the bidirectional optimization similarity matching method.4) In this study,an online model updating algorithm is proposed,which can be used to determine the accuracy of the model.