발간년도 : [2023]
| 논문정보 |
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| 논문명(한글) |
[Vol.18, No.2] Object Volume Analysis Algorithm for Automatic Detection of Visitors |
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| 논문투고자 |
Kwon Oh-Sung |
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| 논문내용 |
A technology that automatically detects the number of people entering and exiting a building is necessary for the evacuation of victims in the event of a disaster such as a fire. As a solution for this, this paper proposes a method of automatically detecting the volume area of entry and exit personnel in real time using a thermal imaging camera installed at the entrance. The first step of such a detection system is to predict the exact number of people in an image, and the YoloV5 deep learning module was applied in this paper. The next step is the moving direction analysis algorithm of the thermal imaging object proposed in this paper. This algorithm analyzes the sequence of sequentially input thermal volume images and counts movement by distinguishing between incoming and outgoing people. The proposed algorithm divides the main area where the occupant moves in the image into 4 subareas and tracks the shape of the object volume and distribution change in each subarea. The proposed method showed stable detection performance even with various movement variations of pedestrians occurring at the entrance. The occupant analysis in this way is performed for each entrance of the building, and the total number of occupants in the building is estimated by adding the number of people entering and leaving the building as a whole. As a result of the experiment, when the proposed motion detection algorithm of the object was applied after YoloV5 analysis, real-time high-speed processing was possible and improved detection performance was confirmed compared to the existing centerline detection method. |
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