Objective

2

Visual tracking is an important research direction in the field of computer vision and is widely applied in intelligent transportation

human-computer interaction

and other areas. Correlation filter-based trackers (CFTs) have achieved excellent performance due to their efficiency and robustness in tracking field. However

the design of a robust tracking algorithm for complex dynamic scenes is challenging due to the influence of lighting

fast motion

background interference

target rotation

scale change

occlusion

and other factors. In addition

the selection and presentation of features are constantly used as the primary considerations in establishing a target appearance model during tracking. To improve the robustness of the appearance model

many trackers introduce gradient feature

color feature

or several other combined features rather than a single gray feature. However

they do not discuss the role of each feature and their relationships in the model.

Method

2

The research on correlation filter theory achieves remarkable improvements. On the basis of this research

the appearance model is used to represent the target and verify the observation. This process is the most important part of any tracking algorithm. Moreover

the features are fundamental and difficult in appearance representation. Therefore

this study mainly focuses on the selection and combination of features. Gradient feature

color feature

and raw pixel have been discussed in previous works. As a common descriptor of shape and edge

gradient feature is invariable in translation and light and performs well in the tracking scene of deformation

light change

and partial occlusion. However

the gradient feature of the target is not evident

and the description capability of the feature is weakened when considerable noise is encountered in the background

target rotation

and target blur. The color of the target and background can be distinguished although they are usually different. On this basis

a new tracking method called weighted multi-feature fusion (WMFF) tracker is proposed via the introduction of a weight vector to fuse multiple feature on the appearance model. The model is dominated by gradient features and is supplemented by color feature and original pixels

which can compensate the inadequacies of single-gradient feature and provide the utilization of the color features of color

thereby making features complementary to each other. In detail

this study constructs a three-variable linear equation on weights based on the calculation method of each feature. The proportional relationships in this equation are solved rather than their specific values. The gradient feature can transform the solutions of weight vector to determine the proportional coefficients of each feature by using it as a criterion. Therefore

the equation is a system of linear equations of two unknowns. In addition

the equation has a limited integer solution set

and the final proportion coefficient is determined by experimental verification on test sequence in terms of the dimension information of feature calculation. This method normalizes the proportion coefficient as weight vector and builds a new weighted feature-mixing model of target appearance to model. The WMFF tracker adopts a detection-based tracking framework

which includes feature extraction

model construction

filter training

target center detection

and model update.

Result

2

A total of 100 video sequences from the object tracking benchmark datasets (herein

OTB-100 datasets) are adopted in the experiments to compare the performance with seven other state-of-the-art trackers

which include five CFTs. A total of 11 different attributes

such as illumination

occlusion

and scale variation

are annotated on video sequences. Comparisons and analyses are performed for these trackers by using precision

average center error

average Pascal VOC overlap ratio

and median frame per second as evaluation standards. Precision and success plots of different datasets are also presented

and the performance of different attributes are discussed. Experimental results on benchmark OTB-100 datasets demonstrate that our tracker can achieve real-time and better performance compared with other methods

especially on Basketball

DragonBaby

Panda

and Lemming sequences. The edge contours

especially the gradient information of the target

are unremarkable when the scene is subjected to motion blur due to occlusion or deformation

which causes the appearance model constructed by the gradient feature not being able to distinguish the target accurately and thus tracking failure easily occurs. Meanwhile

the WMFF tracker can utilize the color feature as a supplement to construct the appearance model in time to obtain a robust tracking effect when the gradient feature is invalid. The color feature has the same level of importance as the gradient feature and achieves an ideal feature combination effect. The performance of the proposed method outperforms other algorithms on multiple datasets

and the average results on OTB-100 datasets show that the precision is improved by 1.2% compared with a scale-adaptive kernel CFT with feature integration tracker.

Conclusion

2

In this study

a weight vector is introduced to combine features in describing the appearance of the target

and a WMFF tracker is proposed based on a CFT framework. A new hybrid feature HCG is dominated by gradient feature and is supplemented by color and gray feature

which can be used to model the appearance of the target. This model can compensate the deficiency of single feature and enables the function of each feature. This model not only can make the features complement one another but also make the appearance model adapt to multiple complex scenes. The WMFF tracker makes the tracking length longer than other trackers in complex dynamic scenes and improves the robustness of the algorithm.