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. 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. 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%. 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.