Under a complex background

a majority of the traditional model-matching tracking methods only consider the characteristics of the moving target without fully utilizing the relationship between the moving target and the image for object tracking

especially when the target was occluded during the process of object tracking. Consequently

these methods allow the moving target to drift easily; as a result

the moving target is sometimes lost. To solve these problems

a novel object-tracking approach based on foreground discrimination of local model matching is proposed. First

the algorithm selects previous frames of the image frame sequences for tracking training

and each image frame is divided into superpixel blocks. Second

the vector cluster is composed of all superpixel blocks

and the object model that contains superpixel blocks is established by the discrimination appearance model. Finally

the algorithm takes the object model as a matching model

adopts expectation maximization to estimate the foreground information

and utilizes foreground discrimination to match the local model. Hence

the tracking object is determined. Compared with other excellent tracking algorithms

the proposed target-tracking algorithm can accurately and effectively adapt to the complex changes in the target states of a video scene through foreground discrimination and local model matching and can adequately solve the problems of tracking drift under various uncertain factors. This algorithm can also achieve the same or even higher tracking accuracy compared with existing model-matching tracking methods. For the video sequences of Girl

Lemming

Liquor

Shop

Woman

Bolt

CarDark

David

and Basketball

the average center errors are 9.76

28.65

19.41

5.22

8.26

7.69

8.13

11.36

and 7.66

respectively

and the tracking overlap ratios are 0.69

0.61

0.77

0.74

0.80

0.79

0.79

0.75

and 0.69

respectively. Experiment results indicate that the proposed target-tracking algorithm can adaptively update noise model parameters in real time

accurately estimate the foreground information of images according to different image sequences

eliminate background information interference

and achieve tracking accuracy and adaptability under the conditions of partial occlusion

target deformation

illumination changes

and complex background.