Chapter
Jan 25, 2024

Identifying CCTV Surveillance Coverage Using MMS-Acquired Point Cloud and CCTV Images

Publication: Computing in Civil Engineering 2023

ABSTRACT

Smart city projects aim to enhance public safety and disaster management through the use of technology such as CCTV (closed-circuit television) cameras. However, sharing CCTV surveillance areas across local and central governments can be challenging due to the different models and physical environment surrounding each camera. In this study, we propose a method to extract camera parameters and surveillance areas for creating a visible coverage map of CCTV without the initial value of IOP (interior orientation parameters). The EOP (exterior orientation parameters) and focal length of CCTV cameras were extracted using point cloud data obtained through an MMS (mobile mapping system) using perspective projection algorithms. The surveillance areas were then vectorized utilizing a combination of DBSCAN (density-based spatial clustering of applications with noise) and convex hull algorithms, with the SMRF (simple morphological filter) algorithm used to account for occlusion areas. Manual verification was utilized to ascertain the ground truth of the surveillance coverage. Comparing the ground truth with the estimated surveillance area vector data, an IoU (Intersection of Union) of over 85% was recorded, demonstrating the validity of the proposed method for creating accurate CCTV visibility areas. Our study presents a method for developing CCTV surveillance zones in smart city initiatives, which can facilitate the exchange of vector data between local and central governments. Our results offer a holistic perspective on a situation, which can aid in improving public safety and disaster management.

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Go to Computing in Civil Engineering 2023
Computing in Civil Engineering 2023
Pages: 421 - 428

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Published online: Jan 25, 2024

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Wonjun Choi [email protected]
1GRS Laboratory, Dept. of Civil and Environmental Engineering, Yonsei Univ. ORCID: https://orcid.org/0000-0002-4674-4211. Email: [email protected]
Youngmok Kwon [email protected]
2GRS Laboratory, Dept. of Civil and Environmental Engineering, Yonsei Univ.Email: [email protected]
3GRS Laboratory, Dept. of Civil and Environmental Engineering, Yonsei Univ.Email: [email protected]
CheolHwan Kim [email protected]
4GRS Laboratory, Dept. of Civil and Environmental Engineering, Yonsei Univ.Email: [email protected]
Hong-Gyoo Sohn [email protected]
5Professor, Dept. of Civil Engineering, Yonsei Univ., Seoul, South Korea. ORCID: https://orcid.org/0000-0003-1839-3431. Email: [email protected]

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