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.