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收稿：2020-01-21，
修回：2020-4-14，
录用：2020-4-21，
纸质出版：2021-02-16
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目的
2
目标遮挡一直是限制跟踪算法精确度和稳定性的问题之一，针对该问题，提出一种抗遮挡的多层次重定位目标跟踪算法。
方法
2
通过平均峰值相关能量动态分配特征权重，将梯度特征与颜色直方图特征动态地结合起来进行目标跟踪。利用多峰值检测和峰值波动情况进行目标状态判定，若目标状态不理想，则停止模板更新，避免逐帧更新导致目标漂移，继续跟踪目标；若判定目标遮挡，则提取对应特征点，使用最邻近距离比进行特征匹配和筛选，丢弃负样本的最邻近样本作为二次筛选，利用广义霍夫变换进行第3次筛选并重定位目标，对目标继续跟踪。
结果
2
在标准数据集OTB（object tracking benchmark）100和LaSOT（large-scale single object tracking）上的实验结果显示，本文算法的精确率分别为0.885和0.301，相较于Staple算法分别提升了13.5%和30.3%。
结论
2
在目标发生遮挡的场景中，本文方法能够重定位目标并且继续跟踪，优化后的模板更新策略提高了算法速度。目标状态的判定有效估计了目标遮挡问题，可以及时采取应对策略，提高算法在复杂环境下的稳定性。
Objective
2
As one of the important research directions in the field of computer vision
target tracking has a wide range of applications in the fields of video surveillance
human computer interaction
and behavior analysis. A tracking algorithm analyzes target location information in real time in a subsequent sequence of video images by giving the target information (i.e.
location and size) in the first frame. At present
target tracking technology has achieved considerable progress
but the robustness of real-time tracking algorithms is still affected by factors
such as target occlusion
illumination change
scale change
fast motion
and background interference. Among these issues
the occlusion problem is the most prominent. A complementary learning correlation filter tracking algorithm updates the template frame by frame. The reliability of the sample is not discriminated during template update
and the sample is not filtered. When background information is complex
particularly when the target is occluded
the template update result will gradually deviate from the target to be tracked. In particular
the color feature is more susceptible to complex environmental factors
aggravating target drift
and thus
template update leads to target drift and occlusion.
Method
2
The problem of losing the target persists. The occlusion problem has always limited the accuracy and stability of tracking algorithms. To address this problem
an anti-occlusion multilevel retargeting target tracking algorithm is proposed. This algorithm has three innovations.1) By using the average peak correlation energy
the gradient and color histogram features are dynamically combined to distribute weight reasonably. 2) The target state is determined in real time through peak responses and fluctuations
and the template update strategy is optimized. 3) To address the occlusion problem during the tracking process
a multilevel target relocation strategy is proposed and multilevel filtered feature points are used in the target relocation operation. Feature weight is determined on the basis of the dynamically changing average peak correlation energy
and it is used to combine the gradient and color histogram features for target tracking. After the current frame identifies the target position
target state determination is performed using multi-peak detection and the peak fluctuation condition. If the target state is not ideal
then template update is stopped. Frame-by-frame update is avoided
causing the target to drift
and then target tracking is continued. If target occlusion is determined
then the oriented fast and rotated brief feature of the target is extracted. The nearest neighbor distance ratio of the feature points is matched and filtered
and the nearest neighbor of the negative sample is discarded as secondary screening. Third screening is performed via the generalized Hough transform
the target is relocated
and tracking the target is continued.
Result
2
To objectively verify the advantages and disadvantages of the proposed algorithm
10 groups of image sequences
namely
Basketball
Bird2
CarDark
CarScale
DragonBaby
Girl
Human5
Human8
Singer1
and Walking2
are selected. Nine algorithms
including the proposed algorithm
are selected for the tracking experiments. The eight other algorithms are as follows: kernel correlation filters
discriminative scale space tracking
staple
background-aware correlation filter
spatially regularized correlation filter
scale adaptive multiple features
efficient convolution operators
and spatiotemporal regularized correlation filter. Experimental results for the standard datasets OTB(object tracking benchmark)100 and LaSOT(large-scale single object tracking) show that the accuracy of the algorithm proposed in this study is 0.885 and 0.301
which are 13.5% and 30.3% higher than the original algorithm
respectively.
Conclusion
2
In the scenario where in the target is occluded
the target can be repositioned and tracking continues. The optimized template update strategy increases the speed of the algorithm. The determination of the target state effectively estimates the target occlusion problem and can adopt a timely coping strategy to improve the stability of the algorithm in a complex environment.
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