论文标题
有高效的猪计数在人群中具有跟踪和空间感知的时间响应过滤
Efficient Pig Counting in Crowds with Keypoints Tracking and Spatial-aware Temporal Response Filtering
论文作者
论文摘要
猪计数是大型猪养殖的至关重要的任务，通常在视觉上由人类完成。但是这个过程非常耗时且容易出错。文献中很少有研究开发自动猪计数方法。现有的方法仅针对使用单图像进行猪计数，其准确性受到多种因素的挑战，包括猪运动，遮挡和重叠。尤其是，单个图像的视野非常有限，无法满足大型猪分组房屋的猪计数要求。为此，我们仅使用一台单眼鱼眼摄像机和检查机器人在人群中展示了一个实时自动猪计数系统。我们的系统表明，它会产生超过人类的准确结果。我们的管道始于一种新型的自下而上的猪检测算法，以避免由于猪的重叠，遮挡和变形而导致的假阴性。深度卷积神经网络（CNN）旨在检测猪体部位的关键点并将关键点关联以识别单个猪。之后，使用有效的在线跟踪方法将猪跨视频帧关联。最后，提出了一种新型的空间感知时间响应滤波（STRF）方法来预测猪的计数，这有效抑制由猪或相机运动或跟踪失败引起的误报。整个管道已部署在边缘计算设备中，并证明了有效性。
Pig counting is a crucial task for large-scale pig farming, which is usually completed by human visually. But this process is very time-consuming and error-prone. Few studies in literature developed automated pig counting method. Existing methods only focused on pig counting using single image, and its accuracy is challenged by several factors, including pig movements, occlusion and overlapping. Especially, the field of view of a single image is very limited, and could not meet the requirements of pig counting for large pig grouping houses. To that end, we presented a real-time automated pig counting system in crowds using only one monocular fisheye camera with an inspection robot. Our system showed that it produces accurate results surpassing human. Our pipeline began with a novel bottom-up pig detection algorithm to avoid false negatives due to overlapping, occlusion and deformation of pigs. A deep convolution neural network (CNN) is designed to detect keypoints of pig body part and associate the keypoints to identify individual pigs. After that, an efficient on-line tracking method is used to associate pigs across video frames. Finally, a novel spatial-aware temporal response filtering (STRF) method is proposed to predict the counts of pigs, which is effective to suppress false positives caused by pig or camera movements or tracking failures. The whole pipeline has been deployed in an edge computing device, and demonstrated the effectiveness.
