摘要:
为解决相关滤波类视觉跟踪算法中的边界效应问题，提出一种基于自适应空间正则化的视觉跟踪算法.在经典滤波模型中引入自适应空间正则化项，通过建立正则权重在相邻帧之间的关联，自适应调整当前帧的模型正则化权重，减小边界效应的影响.采用自适应宽高比的尺度估计策略，以及基于颜色直方图相似度的模型更新策略，抑制模型漂移，提高跟踪准确性.实验显示，该算法在UAV123，OTB2013，OTB2015这3个数据集上的跟踪成功率和精确度均高于所有对比的算法，且即使在复杂场景中也能保持良好的跟踪效果.特别是在出现运动模糊和目标在平面内旋转2种情况时，该算法的跟踪成功率较排名第2的算法分别提升了9.72个百分点和9.03个百分点，说明所提出的算法具有较好的适应性.
Abstract:
In the visual tracking algorithm based on correlation filters, the method of generating sample sets by cyclic shift greatly reduces the amount of calculation. However, it will also bring about boundary effects, and the resulting error samples will weaken the discriminative ability of the classifier. In order to solve the above problem, a visual tracking algorithm based on adaptive spatial regularization is proposed. An adaptive spatial regularization term is introduced into the classic filtering model. By establishing the correlation of regularization weights between adjacent frames, the regularization weights of the model can be adaptively adjusted. In this way, the risk of overfitting when processing unreal samples can be reduced, thereby mitigating the boundary effect. We adopt a scale estimation strategy with adaptive aspect ratio, which can accurately track the scale change of the target. In addition, the update strategy based on the similarity of color histograms is used to avoid the model update when the tracking is inaccurate, thereby suppressing model drift and improving tracking accuracy and speed. Experiments show that the success rate and accuracy of our algorithm on UAV123, OTB2013, OTB2015 are higher than all the compared algorithms. And even in various complex scenes, our algorithm can still maintain a high tracking success rate. Especially in the presence of motion blur and in-plane rotation, the success rate scores are 9.72% and 9.03% higher than the second best algorithm, respectively, which shows that the algorithm has good adaptability.
百度学术
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