Abstract
In the blooming era of smart edge devices, surveillance cameras have been deployed in many locations. Surveillance cameras are most useful when they are spaced out to maximize coverage of an area. However, deciding where to place cameras is an NP-hard problem and researchers have proposed heuristic solutions. Existing work does not consider a significant restriction of computer vision: in order to track a moving object, the object must occupy enough pixels. The number of pixels depends on many factors (How far away is the object? What is the camera resolution? What is the focal length?). In this study, we propose a camera placement method that identifies effective camera placement in arbitrary spaces and can account for different camera types as well. Our strategy represents spaces as polygons, then uses a greedy algorithm to partition the polygons and determine the cameras’ locations to provide the desired coverage. Our solution also makes it possible to perform object tracking via overlapping camera placement. Our method is evaluated against complex shapes and real-world museum floor plans, achieving up to 85% coverage and 25% overlap.
Highlights
Smart edge devices equipped with cameras are utilized in automated surveillance systems to gather visual data and process the data with computer vision techniques, such as object detection or tracking
Existing work on camera placement assumes that a surveillance camera can see infinitely far away
Our method provides less than 100% coverage (82% coverage) because the subpolygons created by the algorithm do not always match triangle shapes of the camera field of coverage (FOC); as a result, the proposed method has overlaps (12% overlap), which could be potentially useful for object tracking applications during camera hand-off
Summary
Smart edge devices equipped with cameras are utilized in automated surveillance systems to gather visual data and process the data with computer vision techniques, such as object detection or tracking. Existing work on camera placement assumes that a surveillance camera can see infinitely far away (similar to the original “art gallery problem”). Most tracking applications struggle to detect objects at low resolutions (below 10 pixels per foot) [3]. Visibility is not sufficient for automated persistent tracking This restriction is further complicated by the fact that surveillance cameras come in all types: differing focal lengths and camera resolutions all have an impact. This paper proposes a greedy solution for determining an effective placement of cameras for monitoring an area with a target resolution sufficient for computerized tracking of individuals
Sign-in/Register to view highlights & summary Sign-in/Register to access full text options 
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 callDisclaimer: All third-party content on this website/platform is and will remain the property of their respective owners and is provided on "as is" basis without any warranties, express or implied. Use of third-party content does not indicate any affiliation, sponsorship with or endorsement by them. Any references to third-party content is to identify the corresponding services and shall be considered fair use under The CopyrightLaw.
