Image Based People Counting System for High Dense crowd
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Date
2023
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Abstract
The main objective of crowd counting is to count people in crowd images accurately. Contemporary methodologies use multi-column CNN designs to regress density maps of crowd images in order to operate the scale or perspective shifts which frequently appear in crowd images. Considering advances in technology, low-cost camera surveillance is already widely deployed. They are used for surveillance in important locations like buildings and businesses as well as in common areas like parks, schools, railways, and airports. Typically, the things in question are moving people or moving automobiles. Crowd analysis is essential in safety evaluation and other related domains. But most of the work was done by people up until this point. Automatic assessment of crowds is becoming more and more necessary when there is a lot of photographic equipment because manual approaches may become unreliable or expensive. Population counting is the first aspect of crowd evaluation that may be dealt: with as a computer vision problem. It creates persons in the image using a data image.Such detection-based systems have the drawback that the performance of the detector is strongly impacted by the presence of people in congested areas or large crowds, which reduces the accuracy of the final estimation. People have suggested clustering the trajectories of monitored visual characteristics in order to count. crowds in videos. Various columns’ reception areas respond differently to different. sized people’s bodies.our main objective is to make a system that can count high dense crowds accurately by using images of the people.
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CNN, low-cost camera, crowd analysis, videos