Continuously tracking and see-through occlusion based on a new hybrid synthetic aperture imaging model
Continuously tracking and see-through occlusion based on a new hybrid synthetic aperture imaging model
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DOI：
10.1109/cvpr.2011.5995417
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发表时间：
2011-06
期刊：
影响因子：
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通讯作者：
Tao Yang;Yanning Zhang;Xiaomin Tong;Xiaoqiang Zhang;Rui Yu
中科院分区：
文献类型：
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作者：
Tao Yang;Yanning Zhang;Xiaomin Tong;Xiaoqiang Zhang;Rui Yu
对于许多计算机视觉应用来说，在严重遮挡的混乱拥挤场景中对多人进行鲁棒检测和跟踪是一项具有挑战性的任务。本文提出了一种新的混合合成孔径成像模型来解决这一问题。该方法的主要特点包括：(1)据我们所知，该算法首次在联合多相机合成孔径成像领域解决被遮挡人的成像与跟踪问题。(2)设计多模型框架，实现检测、成像、跟踪模块之间的无缝交互。(3)在目标检测模块中，提出了一种基于多约束的三维前景轮廓合成孔径成像体中人物定位和鬼影目标去除方法。(4)在合成成像模块中，提出了一种新的基于遮挡物去除的合成成像方法，即使在严重遮挡的情况下也能连续获得目标清晰图像。(5)在目标跟踪模块中，采用相机阵列对彩色合成孔径图像进行鲁棒跟踪。建立了一种基于网络摄像机的混合合成孔径成像系统，定性和定量分析的实验结果表明，该方法可以在挑战场景中可靠地定位和看到人。
Robust detection and tracking of multiple people in cluttered and crowded scenes with severe occlusion is a significant challenging task for many computer vision applications. In this paper, we present a novel hybrid synthetic aperture imaging model to solve this problem. The main characteristics of this approach include: (1) To the best of our knowledge, this algorithm is the first time to solve the occluded people imaging and tracking problem in a joint multiple camera synthetic aperture imaging domain. (2) A multiple model framework is designed to achieve seamless interaction among the detection, imaging and tracking modules. (3)In the object detection module, a multiple constraints based approach is presented for people localizing and ghost objects removal in a 3D foreground silhouette synthetic aperture imaging volume. (4) In the synthetic imaging module, a novel occluder removal based synthetic imaging approach is proposed to continuously obtain object clear image even under severe occlusion. (5) In the object tracking module, a camera array is used for robust people tracking in color synthetic aperture images. A network camera based hybrid synthetic aperture imaging system has been set up, and experimental results with qualitative and quantitative analysis demonstrate that the method can reliably locate and see people in challenge scene.