计算机科学 ›› 2019, Vol. 46 ›› Issue (3): 137-141.doi: 10.11896/j.issn.1002-137X.2019.03.020
范蓉蓉,樊佳庆,刘青山
FAN Rong-rong, FAN Jia-qing, LIU Qing-shan
摘要: 为解决补充学习跟踪算法(Staple)在目标被部分遮挡时存在的跟踪失败问题,文中提出了一种简单而有效的高置信度补充学习跟踪算法(High-confidence updata Complementary Learner Tracker,HCLT)。首先,输入当前帧,得到标准相关滤波分类器的检测响应值;然后,计算相关滤波响应的置信度,若计算结果大于阈值则当前帧更新滤波器,否则停止更新;接着,计算出持续不更新的帧数,如果有连续10帧不更新,则强制更新;最后,通过融合颜色补充学习器的响应,得到总的响应结果,其中,响应中最大值的位置即为跟踪结果。将所提算法与补充学习跟踪(Correlation Filter Net tracker,Staple)、端到端表示跟踪(CFNet)、注意力相关滤波网络跟踪(Attentional Correlation Filter Network tracker,ACFN)和对冲深度跟踪(Hedged Deep Tracking,HDT)等先进算法进行实验对比。在OTB100和VOT2016数据集上的结果表明,所提算法在成功率和预期覆盖率方面分别超过基准补充学习跟踪算法(Staple)1.0个百分点和0.4个百分点。另外,在严重遮挡和剧烈光照变化的视频集上的良好表现也充分说明了所提算法在处理表观剧烈变化的情况时很有效。
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