A practical method for counting arbitrary target objects in arbitrary scenes
A practical method for counting arbitrary target objects in arbitrary scenes
复制标题
一种实用的任意场景中任意目标物体计数方法
DOI：
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发表时间：
2013
期刊：
影响因子：
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通讯作者：
Jiebo Luo
中科院分区：
文献类型：
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作者：
Yao Zhou;Jiebo Luo
对物体进行计数，例如估计显微图像中的细胞数量或监控视频中的行人数量，通常使用计数检测方法来完成。然而，这种方法需要明确的对象建模和对象检测，因此经常遇到场景中物体之间相互遮挡的问题。我们扩展了一个有监督的学习框架，它绕过了目标检测中的挑战，而是专注于估计目标密度，其在图像区域上的积分可以快速产生目标的计数。我们的扩展使其适用于任意对象和场景。特别是，我们通过场景中物体的运动流自动确定感兴趣的区域，并在感知到存在时补偿透视效果。使用细胞、人群、交通和鸟类数据集进行的大量实验表明，该方法在安全、交通、生物医学、生态、环境和城市规划等广泛的人类受益应用中具有鲁棒性。
Counting objects such as estimating the number of cells in a microscopic image or the number of pedestrians in a surveillance video is usually accomplished using a counting by detection approach. However, such approaches require explicit object modeling and object detection, and thus often run into problems in the presence of mutual occlusion between objects in the scene. We extend a supervised learning framework that bypasses the challenges in object detection and instead focuses on estimating an object density whose integral over an image region quickly yields the count of objects. Our extensions make it practical for arbitrary objects and scenes. In particular, we automatically determine the area of interest through the motion flow of the objects in the scene, and compensate for perspective effect when it is sensed to be present. Extensive experiments using cells, crowds, traffic, and birds data sets have shown the robustness of the method for a wide range of humanity benefitting applications including security, transportation, biomedicine, ecology, environment, and urban planning.