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. 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. 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. 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.