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联合特征融合和判别性外观模型的多目标跟踪

DOI: 10.11834/jig.20150906

Keywords: 多目标跟踪,判别性外观模型,Adaboost,时间滑动窗

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Abstract:

目的针对基于检测的目标跟踪问题,提出一种联合多特征融合和判别性外观模型的多目标跟踪算法。方法对时间滑动窗内的检测器输出响应,采用双阈值法对相邻帧目标进行初级关联,形成可靠的跟踪片,从中提取训练样本;融合多个特征对样本进行鲁棒表达,利用Adaboost算法在线训练分类器,形成目标的判别性外观模型;再利用该模型对可靠的跟踪片进行多次迭代关联,形成目标完整的轨迹。结果4个视频数据库的目标跟踪结果表明,本文算法能较好的处理目标间遮挡、目标自身形变,以及背景干扰。对TUD-Crossing数据库的跟踪结果进行了定量分析,本文算法的FAF(跟踪视频序列时,平均每帧被错误跟踪的目标数)为0.21、MT(在整个序列中,有超过80%视频帧被跟踪成功目标数占视频序列目标总数的比例)为84.6%、ML(在整个序列中,有低于20%视频帧被跟踪成功目标数占视频序列目标总数的比例)为7.7%、Frag(视频序列目标真值所对应轨迹在跟踪中断开的次数)为9、IDS(在跟踪中,目标身份的改变次数)为4;与其他同类型多目标跟踪算法相比,本文算法在FAF和Frag两个评估参数上表现出色。结论融合特征能对目标进行较为全面的表达、判别性外观模型能有效地应用于跟踪片关联,本文算法能实现复杂场景下的多目标跟踪,且可以应用到一些高级算法的预处理中,如行为识别中的轨迹检索。

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