Visual object tracking is a fundamental problem in computer vision that has numerous applications

such as in intelligent visual surveillance

human-machine interaction

and content-based video coding.In a generic tracking problem

the target can be any object and only its initial location is known.Most state-of-the-art approaches address the tracking problem by learning a discriminative appearance model for the target object.Among the discriminative tracking methods

correlation filter-based approaches have recently demonstrated an excellent performance on benchmark-tracking datasets.Despite the significant developments made in recent decades

visual tracking remains a challenging problem

mainly due to the considerable appearance changes caused by occlusion

deformation

abrupt motion

illumination variation

and background clutter.Features based on convolutional neural networks (CNNs) have recently demonstrated state-of-the-art results on a wide range of visual recognition tasks.Therefore

understanding how to best utilize the rich feature hierarchies in CNNs is significant in robust visual tracking.In view of the problem of a fast-moving

scaling

and rotating target during tracking

a kernel correlation adaptive target-tracking approach based on a convolution feature was proposed in this study. A CNN was introduced to extract high-and low-convolution features.High-and low-convolution response maps were obtained using the kernel correlation filter algorithm proposed in this study

and target position was estimated using the coarse-to-fine method.The target scale was estimated with a 1D scale correlation filter to realize adaptive target tracking

and the kernel correlation filter was updated in real time. We tested the proposed algorithm in an experiment on typical video sequences in public data sets.These data sets involved challenging factors

such as illumination change

partial occlusion

scale change

and complex background.We compared our method with excellent tracking algorithms

such as high-speed tracking with kernelized correlation filters

adaptive color attributes for real-time visual tracking

and real-time compressive tracking.For a quantitative comparison

we used two evaluation metrics

namely

the average center error and the average overlap ratio.The results of the target-tracking experiment showed that the proposed filter algorithm exhibited better performance than the original comparative filter algorithm.The average central position error was reduced by 20%

and the average overlap rate was increased by 12%.The center errors in the video sequences Singer1

Car4

Jogging

Girl

Football

and MotorRolling were 8.71

6.83

3.96

3.91

4.83

and 9.23

respectively.The tracking overlap ratios of the aforementioned video sequences were 0.969

1.00

0.967

0.994

0.967

and 0.512

respectively. In this study

a CNN was introduced to extract high-and low-convolution features.The high-convolution features were used to determine the target and background

whereas the low-convolution features were adopted to predict target position.The coarse-to-fine method was applied to accurately locate target position

which solved the problem of a large tracking error due to the rotation and change in scale of the target.This method improves tracking performance and can update learning in real time.The experimental results indicate that the proposed approach maintains its good robustness and high adaptability even in elaborate scenes

such as those with a changing or fast-moving target scale

occlusion

and various illumination conditions.