目的 针对现有的超像素目标跟踪算法（RST）对同一类中分别属于目标和相似干扰物体的超像素块赋予相同特征置信度，导致难以区分目标和相似干扰物的问题，为此提出自适应紧致特征的超像素目标跟踪算法（ACFST）。方法 该方法在每帧的目标搜索区域内构建适合目标大小的自适应紧致搜索区域，并将该区域内外的特征置信度分别保持不变和降低。处于背景中的相似干扰物体会被该方法划分到紧致搜索区域外，其特征置信度被降低。当依据贝叶斯推理框架求出对应最大后验概率的目标时，紧致搜索区域外的特征置信度低，干扰物体归属目标的程度也低，不会被误判为目标。结果 在具有与目标相似干扰物体的两个视频集进行测试，本文ACFST跟踪算法与RST跟踪算法相比，平均中心误差分别缩减到5.4像素和7.5像素，成功率均提高了11%，精确率分别提高了10.6%和21.6%，使得跟踪结果更精确。结论 本文提出构建自适应紧致搜索区域，并通过设置自适应的参数控制紧致搜索区域变化，减少因干扰物体与目标之间相似而带来的误判。在具有相似物体干扰物的视频集上验证了本文算法的有效性，实验结果表明，本文算法在相似干扰物体靠近或与目标部分重叠时，能够保证算法精确地跟踪到目标，提高算法的跟踪精度，具有较强的鲁棒性，使得算法更能适应背景杂乱、目标遮挡、形变等复杂环境。

Objective Object tracking is the basic theory of computer vision that has been given increasing attention.Object tracking encounters several natural challenges,such as illumination change,scale variations,occlusion,deformable,fast motion,random movement,object presence,analogues or busy background,and low resolution.Recently,superpixel to model object appearance has been employed for object tracking.However,existing superpixel object tracking algorithms(RST) have provided uniform feature confidence to superpixel blocks belonging to the object and similar interference objects in same category,which is difficultly distinguished between object and similar interference objects.A superpixel tracking algorithm with adaptive compact feature(ACFST) is proposed to solve similar interference objects.Method In every frame,the surrounding region of the target is segmented to many superpixels and each superpixel has feature confidence due to the objective model in the last frame.The new method creates a smaller compact search scope to adapt to the object size,and then the feature confidence corresponding to superpixels inside the scope remained unchanged,and the outside scope had decreased.The size of the compact region is controlled by a set of parameters whose values adapt with every change of each frame.The similar interference objects in the background around the object are partitions into the outside of compact search scope and marked as inadequate objective.As such,the feature confidence of the superpixels in interference objects is decreased to reduce miscalculation.Object is composed of multiple superpixels with different feature confidence.When tracking an object in every frame,the candidate sample around the target location of last frame have different confidence.Then,the Bayesian inference is used to find the sample that correspond to the maximum a posteriori probability estimation in the current frame to be regarded as an object.The feature confidence outside of the scope decreases because of the compact search scope,which means that the degree of interference objects is low so that misjudgment did not occur.Result The proposed tracking algorithm is verified using two video sequences with a background similar to the object,namely,Basketball and Girl.The new superpixel object tracking algorithm(i.e.,ACFST) is compared with the original superpixel tracking algorithm(RST) from three aspects,namely,mean center location error,success rate,and precision ratio.In terms of mean center location error,the proposed algorithm can be significantly reduced to 5.4 pixels and 7.5 pixels in the two sequences.In terms of success rate,the ACFST is 11% higher than the RST.With the location threshold limit,the precision ratio of the ACFST is better than that of the RST in the two sequences,an improvement of 10.6% and 21.6%,respectively.Compared with the RST that do not distinguish similar interference objects,the proposed tracking algorithm produces more accurate tracking results.Conclusion The proposed method creates an adaptive compact region and set adaptive parameters to control the size of the compact region,thereby reducing the misjudgment between the real object and the similar interference objects during tracking,resulting in excellent robustness.The effectiveness of this algorithm is verified in video sets with similar interference objects.Experiment showed that when the similar interference objects disturb the object or overlap the object,the existing superpixel object tracking algorithms fail to track object and the new method could track accurately.The tracking precision of the algorithm is improved and the robustness is strong,which is more suitable for complex environments,such as background clutter,target occlusion,and deformation.