计算机科学 ›› 2020, Vol. 47 ›› Issue (9): 157-162.doi: 10.11896/jsjkx.190800160
赵钦炎1, 李宗民1, 刘玉杰1, 李华2
ZHAO Qin-yan1, LI Zong-min1, LIU Yu-jie1, LI Hua2
摘要： 目标跟踪是计算机视觉领域的一个重要研究方向,针对目前算法对于目标外观变化的鲁棒性较差等问题,提出了一种基于信息熵的级联Siamese网络目标跟踪方法。首先利用孪生神经网络(Siamese network)对第一帧目标模板和当前帧待检测区域提取深度卷积特征,并通过相关性计算响应图;然后根据定义的信息熵和平均峰值系数评价响应图质量,针对质量差的响应图对卷积特征进行模型因子更新;最后利用最终的响应图确定目标位置并计算最佳尺度。在VOT2016,VOT2017数据集上进行实验,结果证明在保证实时运行的基础上所提算法的精度优于其他算法。
中图分类号:
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