Objective

2

Target tracking is one of the basic problems in the field of computer vision. It is widely used in security monitoring

military operations

and automatic driving

among others. Tracking algorithms based on correlation filtering have been developed rapidly in recent years because of their fast and efficient features. However

designing a robust tracking algorithm remains a challenging problem due to background clutter

illumination variation

fast motion

rotation

and other complex factors. Building an effective appearance model is a key factor in tracking the success of an algorithm. The expressions of the current appearance model have two major types. In the first type

the appearance model is based on manual design. The common artificial design appearance model is the histogram of oriented gradients (HOG) feature because this feature can efficiently describe the contour and shape information of a target by calculating the direction gradient of the local area of the detected image. In the second type

the appearance model is based on deep learning. Low-level convolutional features contain rich texture information but are unable to adapt to background changes. High-level convolutional features contain rich semantic information that distinguishes backgrounds from targets even in complex contexts. Different informations of an image are described due to varying features; thus

this study proposes a correlation filter tracking method to achieve the effect of adaptive multifeature fusion.

Method

2

In this work

the DSST(discriminative scale space tracking) correlation filter is adopted as the benchmark algorithm

and conv1 and conv5 of the convolutional neural network (CNN) ImageNet-VGG (visual geometry group)-2 048 are used. First

the HOG feature of the target is extracted. Then

the high-and low-level convolutional features of the target are extracted using the CNN. The characteristic response graph is obtained. The maximum peak and shape of the response graph reflect the accurate information of the tracking results. Second

to evaluate the validity of the feature

using the area ratio of the peak as the new index is proposed to distinguish the confidence level of the correlation response graph. The validity of each feature is evaluated using an adaptive threshold segmentation method. If the peak of the response graph is sharp and the periphery is smooth

then the tracking result is reliable. The weight ratio of the feature fusion is obtained

such that the feature is improved and the fusion coefficient is increased. Lastly

the response graph of each feature is fused in accordance with the fusion coefficient

the final response output is calculated

and the target response position is determined from the maximum response value in the response graph. Scale-dependent filter estimation scales are reintroduced to achieve adaptive target tracking.

Result

2

To effectively evaluate the performance of the proposed method

the algorithm is tested on the public dataset OTB (object tracking benchmark)-2013. The 50 videos mostly contain 11 different challenges encountered in the target tracking process (including background complex

deformation

object disappearance

and scale variation). This study compares the algorithm with seven mainstream algorithms. The accuracy and success rate are used as the evaluation and tracking performance indicators. These algorithms are divided into two major categories: traditional tracking algorithms with representative and top ranks: ASLA (adaptive structural local sparse appearance)

SCM (sparsity-based collaborative model)

TLD (tracking-learning-detection). Correlation filtering algorithms: CFNet (correlation filter networks)

KCF (kernel correlation filter)

DSST

SAMF (scale adaptive with multiple features). The experimental results show that the proposed algorithm achieves the highest success rate and accuracy compared with the other algorithms. The accuracy of the proposed method on the OTB-2013 dataset is 77.8%. Those of the other algorithms are as follows: CFNet (76.1%)

DSST (74.6%)

KCF (73.5%)

SAMF (72.5%)

and the traditional algorithm SCM (67.8%). The success rate of the algorithm proposed in this work is 71.5%. Those of the other algorithms are CFNet (71.4%)

DSST (67.5%)

SAMF (66.2%)

and KCF (61.1%). The algorithm presented in this study increases tracking accuracy by 4% and improves success rate by 6%. From the aforementioned experimental data analysis

the method can effectively improve tracking performance. The proposed method ranks first in terms of accuracy compared with CFNet

DSST

SAMF

and KCF in seven attributes: background clutter

deformation

out-of-view

illumination variation

in-plane rotation

out-of-plane rotation

and fast motion. Compared with the other algorithms

the algorithm proposed in this study achieves the highest success rate in the scenes of nine attributes.

Conclusion

2

Different information of an image are described due to varying features; thus

this study proposes a correlation filter tracking method to achieve the effect of adaptive multifeature fusion. A CNN is used to extract high- and low-layer convolutional and HOG features. The adaptive threshold segmentation method is proposed to evaluate the validity of each feature. The two-layer convolutional and HOG features are adaptively fused. The response graph is fused in accordance with the fusion coefficient using feature validity analysis. Compared with most feature fusion methods that connect features serially or in parallel

this algorithm increases the fusion weight of a single feature with strong discriminability

and the appearance model of the target can be accurately represented. Therefore

the proposed algorithm exhibits strong robustness and tracking accuracy in scenarios with low resolution and scale variation. The presented target tracking method will be further studied in the future under occlusion

motion blur

and fast motion conditions.