  Person counting is considered as a fundamental task in video surveillance.
However, the scenario diversity in practical applications makes it difficult to
exploit a single person counting model for general use. Consequently, engineers
must preview the video stream and manually specify an appropriate person
counting model based on the scenario of camera shot, which is time-consuming,
especially for large-scale deployments. In this paper, we propose a person
counting paradigm that utilizes a scenario classifier to automatically select a
suitable person counting model for each captured frame. First, the input image
is passed through the scenario classifier to obtain a scenario label, which is
then used to allocate the frame to one of five fine-tuned models for person
counting. Additionally, we present five augmentation datasets collected from
different scenarios, including side-view, long-shot, top-view, customized and
crowd, which are also integrated to form a scenario classification dataset
containing 26323 samples. In our comparative experiments, the proposed paradigm
achieves better balance than any single model on the integrated dataset, thus
its generalization in various scenarios has been proved.
