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

Application-Driven AI Paradigm for Person Counting in Various Scenarios