摘要
针对目标跟踪中的目标尺度变换、遮挡、快速运动等问题,提出自步上下文感知的相关滤波跟踪算法。首先在正则化最小二乘分类器中引入目标的全局上下文信息,使得这些上下文信息能够被滤波器所学到,并对目标产生高响应,对上下文信息接近零响应;然后引入自步学习,给每一帧的目标和上下文信息赋予权重,挑选出可靠的目标和上下文信息,更新滤波模板;最后学习得到稳健和高效的外观模型。实验表明本文算法在距离精度(DP)提高了2. 81%,成功率(SR)提高了13. 9%,具有较好的跟踪效果。
Aiming at the problem of target scaling, occlusion and fast movement in target tracking, this paper proposes a self-paced context-aware correlation filter tracking algorithm. First, the global context information of the target is introduced in the regularized least squares classifier so that these context information can be learned by the filter, and a high response to the target and a near-zero response to the context information. Then we introduce self-paced learning, assign weights to the target and context information of each frame, pick out reliable target and context information, and update the filter template. Finally a robust and efficient appearance model is got by learning. Experiments show that the algorithm improves 2.81% in distance accuracy （DP）, improves success rate （SR） by 13.9%, and has a good tracking effect.
作者
张驰
韩立新
徐国夏
ZHANG Chi;HAN Li-xin;XU Guo-xia(College of Computer and Information,Hohai University,Nanjing 211100,China)
出处
《计算机与现代化》
2018年第11期35-39,45,共6页
Computer and Modernization
关键词
目标跟踪
相关滤波
上下文感知
自步学习
target tracking
correlation filtering
context-aware
self-paced learning
