目标计数作为一项基础的技术,在许多领域都有广泛的应用,如人群计数、细胞计数、车辆计数等。随着互联网时代的信息爆炸,视频数据呈指数级增长,如何快速、准确地获得目标的数量是用户普遍关心的主要问题之一。得益于计算机视觉技术的快速发展,基于传统机器学习的计数方法正逐步向基于深度学习的方法转变,并在计数的准确性上取得了实质性的进展。介绍了目标计数的研究背景和应用领域,根据模型任务分类,归纳了三类常用的计数模型框架,并从不同的角度分别介绍了近10年来基于计算机视觉技术的模型方法。然后介绍了在人群计数、细胞计数和车辆计数领域中常用的几种公开数据集,并横向比较了各个模型之间的性能。最后总结了现阶段的目标计数模型还存在的不足,并对未来的研究方向进行了展望。

As a fundamental technique, object counting has broad applications, such as crowd counting, cell counting, and vehicle counting. With the information explosion in the internet era, video data has been growing exponentially. How to obtain the number of objects efficiently and accurately is one of the problems that most users care about. By virtue of the great development of computer vision, the counting methods are gradually turned from the traditional machine learning based methods to deep learning based methods, and the accuracy has been improved substantially. First, this paper introduces the background and applications of object counting. Then according to the model task classification, three counting model frameworks are summarized and the computer vision based counting methods in the recent 10 years are introduced from different aspects. Some public datasets in the fields of crowd counting, cell counting, and vehicle counting are introduced and the performance of various models is compared horizontally. Finally, the challenges to be solved and the prospects for future research are summarized.