Target tracking plays an important role in computer vision

which is widely applied in intelligent traffic

robot vision

and motion capture. Experts and scholars have proposed numerous excellent target tracking algorithms in recent years to avoid the influence of illumination changes

target deformation

partial occlusion (even global occlusion)

complex background

and other factors. One of the popular topics in the field of target tracking is determining how to deal with the change in target contour. A level set can better optimize the topology structure of a target

and thus

many researchers have adopted the level set method for the contour extraction and tracking of targets. In 2004

Freedman used the Bhattacharyya distance and Zhang used the Kullback-Leibler distance in 2005

respectively

to determine the target layout and locate the best candidate region. Accordingly

these researchers combined foreground/background matching flow and proposed a combined flow method. However

these two algorithms depend on the initial target selection. When the initial contour differs from the actual contour of the object

the algorithms will require multiple iterations to converge. Chiverton proposed an online contour tracking algorithm based on the learning model. This algorithm establishes a prior target model through initial target morphology and constrains the contour tracking process by using the target model. Ning proposed an approach that applied the morphological information of the initial delineation of the target to establish the prior model. This researcher also adopted the level set method for the implicit representation of the foreground and background regions of the target information. The distribution area of the foreground/background target is determined using the Bhattacharyya similarity measure to realize accurate tracking. Rathi adopted the geometric active contour model to track a deformed target that was moving fast in the framework of a particle filter. The algorithm does not only achieve affine transformation

but can also accurately estimate the non-affine transform target. The contour extraction methods based on a level set are extensively applied to tracking moving targets. Traditional methods can be easily affected by the local occlusion of other targets and the complex background. A novel tracking approach based on anti-interference contour tracking under the prior model constraint is proposed to solve the aforementioned problems. The proposed approach uses a simple model matching algorithm to track the previous five frames of the image sequences. The training sample set is established based on several super pixel blocks obtained via super pixel segmentation. The super pixel block sets with the same color feature are used to establish the cluster sets by using the mean shift algorithm. The confidence probability of each cluster is then calculated

and a prior model of the target is constructed according to the confidence degree of clusters. Subsequently

the target contour is extracted using the segmentation method of the level set. This study proposes a novel decision-making method to avoid the influences of partial occlusion and complex background. This method determines whether a shape prior model is required to constrain the level set evolution process

and thus

obtain more robust tracking results. Lastly

an appearance model online-updating algorithm is proposed. This algorithm can append the appropriate feature compensations to feature sets to improve the updating accuracy of the appearance model. The algorithm uses the evolution results of shape and color features

and then

the feature loss and redundant feature problems are effectively solved when the target is occluded. Six sets of common video sequences are used in the test to verify the performance of the proposed algorithm. The video sequence covers challenging factors

such as illumination change

partial occlusion

target deformation

and complex background. The algorithm is also compared with available contour tracking algorithms

such as the density matching and level set

the learning distribution metric

joint registration

and active contour segmentation. The proposed contour tracking algorithm can achieve the same or even higher tracking accuracy compared with excellent contour tracking algorithms. The average center errors in the video sequences Fish

Face1

Face2

Shop

Train

and Lemming are 3.46

7.16

3.82

13.42

14.72

and 12.47

respectively. The tracking overlap ratios of the aforementioned video sequences are 0.92

0.74

0.85

0.77

0.73

and 0.82

respectively. The average running speeds in the aforementioned video sequences are 4.27 frame/s

4.03 frame/s

3.11 frame/s

2.94 frame/s

2.16 frame/s

and 1.71 frame/s

respectively. Experiment results indicate that using the prior model constraint of the target and implementing decision-making in the contour extraction process provide the algorithm with accurate tracking and strong adaptability characteristics under the conditions of partial occlusion

target deformation

target rotation

and complex background. The characteristics of the proposed approach are as follows：1) a prior model of the target is built by training the sample set

removing the interference of the non-target information in the image

and providing the prior model with a more accurate description of the target；2) a decision-making method is proposed to judge whether a prior model is required. If the constraints of a prior model must be introduced

then the results of the shape subspace and the evolution in color space are fused in the level set segmentation process；3) an appearance model online-updating algorithm is proposed

which can append the appropriate feature compensations to the feature sets

thereby ensuring the accuracy of the model.