Abstract
Cameras have been widely used in buildings for safety and efficiency surveillance. Especially with the integration of artificial intelligence and building information modeling (BIM), many intelligent applications have emerged. However, optimizing the camera plan in buildings with various geometric and semantic properties remains challenging. This study proposes a smart camera placement framework that can be generally applied in different building scenarios to solve this problem. Firstly, a camera-integrated BIM is constructed based on a systematic literature review (SLR) and Industry Foundation Classes (IFC) schema, followed by a sample-based voxelization method to convert BIM models to semantic voxels. Secondly, a realistic camera coverage analysis method is developed to consider the object's properties, based on which a bi-level formulation is proposed. Finally, an efficient bi-level genetic algorithm (EBGA) is proposed to ensure both optimization accuracy and efficiency. The performance of the proposed placement framework is validated in a study of a multi-purpose laboratory and office floor. The validation demonstrated that EBGA obtained full coverage of essential objects and 21.4% more coverage values over the experience-based method. Compared with two benchmark GAs, EBGA saved 65.2% and 49.7% of computation time correspondingly in the experiment.
Accepted Version (
Free)
Published Version
Talk to us
Join us for a 30 min session where you can share your feedback and ask us any queries you have
Schedule a call