计算机科学 ›› 2020, Vol. 47 ›› Issue (11A): 224-230.doi: 10.11896/jsjkx.200500084
马康1, 娄静涛2, 苏致远1, 李永乐2, 朱愿2
MA Kang1, LOU Jing-tao2, SU Zhi-yuan1, LI Yong-le2, ZHU Yuan2
摘要: 在目标跟踪过程中,改进尺度自适应策略、选择辨别能力强的特征是提高跟踪算法性能的重要途径。为解决核相关滤波算法(Kernel Correlation Filtering,KCF)不能适应目标尺度变化、采用单一的方向梯度直方图(Histogram of Oriented Gra-dient,HOG)特征对目标判别能力有限的问题,通过研究同一目标在不同尺度下相关响应值的大小,在分析大量统计数据的基础上发现其变化规律,提出了一种新的尺度自适应策略,并采取HOG和颜色属性特征(Color Name,CN)线性加权融合的方法提高对目标的判别能力。在OTB数据集上的实验结果表明,所提算法的准确率和成功率相比KCF算法分别提高了8.5%和28.9%,在尺度变化属性视频序列上的准确率和成功率相比KCF算法分别提高了8.1%和38.5%,在其他属性视频序列上的表现也有较大提高,并且跟踪速度达到37.68 fps,可满足实时性要求。
中图分类号:
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