【摘 要】
：
提出了通过联合随机蕨与HOG-LBP特征建立目标模型的方法.首先利用选定目标图像块中的HOG-LBP特征向量完成初始化及在其后的过程中产生新的随机蕨,该随机蕨用于对兴趣目标的检测和跟踪.目标模型的更新通过多实例在线学习与更新蕨池实现.提出的方法在选定6个标准视频序列进行测试,测试结果与现在较流行的OnlineB oosting Tracker、MILTracker等跟踪算法进行了比较和分析.结果表
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提出了通过联合随机蕨与HOG-LBP特征建立目标模型的方法.首先利用选定目标图像块中的HOG-LBP特征向量完成初始化及在其后的过程中产生新的随机蕨,该随机蕨用于对兴趣目标的检测和跟踪.目标模型的更新通过多实例在线学习与更新蕨池实现.提出的方法在选定6个标准视频序列进行测试,测试结果与现在较流行的OnlineB oosting Tracker、MILTracker等跟踪算法进行了比较和分析.结果表明,在各种复杂环境下,本研究提出的方法具备良好的跟踪鲁棒性,在抗局部遮挡和光照变化方面具有一定的优势;同
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