《计算机应用研究》|Application Research of Computers

基于AdaBoost的公交客流量统计算法

Statistical algorithm for bus passenger flow based on AdaBoost

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作者 王璇,李倩丽,宋焕生,孙士杰,崔华
机构 1.长安大学 信息工程学院,西安 710064;2.国家测绘地理信息局第一航测遥感院,西安 710054
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文章编号 1001-3695(2018)03-0949-04
DOI 10.3969/j.issn.1001-3695.2018.03.065
摘要 为解决复杂场景目标识别中伪目标的干扰问题,采用基于AdaBoost分类的方法分析疑似目标的三维轨迹,结合真实目标共有的特征信息,进一步分类真实目标与伪目标。首先,根据深度相机获取的深度图像提取疑似目标的人头区域,利用Kalman滤波跟踪得到二维轨迹;其次,通过摄像机标定将目标的二维轨迹转换为空间中的三维轨迹;最后,利用AdaBoost训练正负样本得到强分类器,进一步分类真实目标与伪目标。实验结果表明,该方法能够有效地提高目标识别的精度,对复杂场景下的目标识别具有良好的适应性。
关键词 AdaBoost分类;3D轨迹;深度相机;卡尔曼滤波;摄像机标定
基金项目 国家自然科学基金资助项目(61572083)
陕西省自然科学基础研究计划资助项目(2015JZ018)
中央高校基本科研业务费资助项目(自然科学类)(310824152009,310824163411)
本文URL http://www.arocmag.com/article/01-2018-03-065.html
英文标题 Statistical algorithm for bus passenger flow based on AdaBoost
作者英文名 Wang Xuan, Li Qianli, Song Huansheng, Sun Shijie, Cui Hua
机构英文名 1.SchoolofInformationEngineering,Chang'anUniversity,Xi'an710064,China;2.TheFirstInstituteofAerophotogrammetry&RemoteSensing,StateBureauofSurveying&Mapping,Xi'an710054,China
英文摘要 In order to solve the interference of pseudo target for the object recognition in complex scenes, this paper used the AdaBoost classifier to analyze the 3D trajectory of suspected object. It combined the feature information of the objects to classify the real objects from the false objects. Firstly, it used a depth camera to obtain the depth image and extracted the head region of suspected objects, and it adopted a Kalman filter to track the 2D trajectory on the image. Secondly, the method converted the 2D trajectory to 3D trajectory in space using the camera calibration. Finally, it applied the AdaBoost classifier to train the positive and negative samples, and it got a strong classifier. It used the strong classifier to further distinguish real targets from pseudo target. Experimental results show that this method can improve the accuracy of object identification effectively. It has a good adaptability for object recognition in complex scenes.
英文关键词 AdaBoost classifier; 3D trajectory; depth image; Kalman filter; camera calibration
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收稿日期 2016/10/10
修回日期 2016/12/14
页码 949-952
中图分类号 TP391.4
文献标志码 A