Papers
Topics
Authors
Recent
2000 character limit reached

Orientation-Constrained System for Lamp Detection in Buildings Based on Computer Vision (2312.11380v1)

Published 18 Dec 2023 in cs.CV and cs.AI

Abstract: Computer vision is used in this work to detect lighting elements in buildings with the goal of improving the accuracy of previous methods to provide a precise inventory of the location and state of lamps. Using the framework developed in our previous works, we introduce two new modifications to enhance the system: first, a constraint on the orientation of the detected poses in the optimization methods for both the initial and the refined estimates based on the geometric information of the building information modelling (BIM) model; second, an additional reprojection error filtering step to discard the erroneous poses introduced with the orientation restrictions, keeping the identification and localization errors low while greatly increasing the number of detections. These~enhancements are tested in five different case studies with more than 30,000 images, with results showing improvements in the number of detections, the percentage of correct model and state identifications, and the distance between detections and reference positions

Definition Search Book Streamline Icon: https://streamlinehq.com
References (39)
  1. Lighting control strategy for energy efficient office lighting system design. Energy Build. 2013, 66, 329–337. doi:10.1016/j.enbuild.2013.07.039.
  2. A review on buildings energy consumption information. Energy Build. 2008, 40, 394–398. doi:10.1016/j.enbuild.2007.03.007.
  3. Simulation tools application for artificial lighting in buildings. Renew. Sustain. Energy Rev. 2018, 82, 3007–3026. doi:10.1016/j.rser.2017.10.035.
  4. Light’s Labour’s Lost: Policies for Energy-Efficient Lighting: In Support of the G8 Plan of Action; OECD/IEA: Paris, France, 2006.
  5. Building information modeling for energy retrofitting—A review. Renew. Sustain. Energy Rev. 2018, 89, 249–260. doi:10.1016/j.rser.2018.03.064.
  6. BPOpt: A framework for BIM-based performance optimization. Energy Build. 2015, 108, 401–412. doi:10.1016/j.enbuild.2015.09.011.
  7. Succar, B. Building information modelling framework: A research and delivery foundation for industry stakeholders. Autom. Constr. 2009, 18, 357–375. doi:10.1016/j.autcon.2008.10.003.
  8. IFC4 Add2 Specification. Available online: http://www.buildingsmart-tech.org/specifications/ifc-releases/ifc4-add2 (accessed on 27 March 2019).
  9. gbXML—An Industry Supported Standard for Storing and Sharing Building Properties between 3D Architectural and Engineering Analysis Software. Available online: http://www.gbxml.org/ (accessed on 27 March 2019).
  10. Building Information Modeling (BIM) for green buildings: A critical review and future directions. Autom. Constr. 2017, 83, 134–148. doi:10.1016/j.autcon.2017.08.024.
  11. BIM-Centric Daylight Profiler for Simulation (BDP4SIM): A methodology for automated product model decomposition and recomposition for climate-based daylighting simulation. Build. Environ. 2012, 58, 114–134. doi:10.1016/j.buildenv.2012.06.021.
  12. Spectral Identification of Lighting Type and Character. Sensors 2010, 10, 3961–3988. doi:10.3390/s100403961.
  13. Intelligent Luminance Control of Lighting Systems Based on Imaging Sensor Feedback. Sensors 2017, 17, 321. doi:10.3390/s17020321.
  14. D22{}^{2}start_FLOATSUPERSCRIPT 2 end_FLOATSUPERSCRIPTCO: Fast and robust registration of 3d textureless objects using the directional chamfer distance. Lect. Notes Comput. Sci. 2015, 9163, 316–328.
  15. Lowe, D.G. Distinctive Image Features from Scale-Invariant Keypoints. Int. J. Comput. Vis. 2004, 60, 91–110. doi:10.1023/B:VISI.0000029664.99615.94.
  16. Speeded-Up Robust Features (SURF). Comput. Vis. Image Underst. 2008, 110, 346–359. doi:10.1016/j.cviu.2007.09.014.
  17. BOLD Features to Detect Texture-less Objects. In Proceedings of the 2013 IEEE International Conference on Computer Vision, Sydney, NSW, Australia, 1–8 December 2013; pp.1265–1272. doi:10.1109/ICCV.2013.160.
  18. BORDER: An Oriented Rectangles Approach to Texture-Less Object Recognition. In Proceedings of the 2016 IEEE Conference on Computer Vision and Pattern Recognition (CVPR), Las Vegas, NV, USA, 27–30 June 2016; pp. 2855–2863.
  19. BIND: Binary Integrated Net Descriptors for Texture-Less Object Recognition. In Proceedings of the 2017 IEEE Conference on Computer Vision and Pattern Recognition (CVPR), Honolulu, HI, USA, 21–26 July 2017; pp. 3020–3028. doi:10.1109/CVPR.2017.322.
  20. Real-time learning and detection of 3D texture-less objects: A scalable approach. In Proceedings of the British Machine Vision Conference; BMVA Press: Durham, UK, 2012; doi:10.5244/C.26.23.
  21. Efficient texture-less object detection for augmented reality guidance. In Proceedings of the 2015 IEEE International Symposium on Mixed and Augmented Reality Workshops, Fukuoka, Japan, 29 September–3 October 2015; pp. 81–86.
  22. Groups of adjacent contour segments for object detection. IEEE Trans. Pattern Anal. Mach. Intell. 2007, 30, 36–51.
  23. From Images to Shape Models for Object Detection. Int. J. Comput. Vis. 2010, 87, 284–303. doi:10.1007/s11263-009-0270-9.
  24. Shape-based recognition of wiry objects. IEEE Trans. Pattern Anal. Mach. Intell. 2004, 26, 1537–1552. doi:10.1109/TPAMI.2004.128.
  25. Parametric Correspondence and Chamfer Matching: Two New Techniques for Image Matching. In Proceedings of the 5th International Joint Conference on Artificial Intelligence—Volume 2; Morgan Kaufmann Publishers Inc.: San Francisco, CA, USA, 1977; pp. 659–663.
  26. Borgefors, G. Hierarchical chamfer matching: A parametric edge matching algorithm. IEEE Trans. Pattern Anal. Mach. Intell. 1988, 10, 849–865. doi:10.1109/34.9107.
  27. Multiscale Categorical Object Recognition Using Contour Fragments. IEEE Trans. Pattern Anal. Mach. Intell. 2008, 30, 1270–1281. doi:10.1109/TPAMI.2007.70772.
  28. Gradient Response Maps for Real-Time Detection of Textureless Objects. IEEE Trans. Pattern Anal. Mach. Intell. 2012, 34, 876–888. doi:10.1109/TPAMI.2011.206.
  29. Fast Directional Chamfer Matching. In Proceedings of the 2010 IEEE Computer Society Conference on Computer Vision and Pattern Recognition, San Francisco, CA, USA, 13–18 June 2010.
  30. Generation of BIM data based on the automatic detection, identification and localization of lamps in buildings. Sustain. Cities Soc. 2018, 36, 59–70. doi:10.1016/j.scs.2017.10.015.
  31. Generalized Vision-Based Detection, Identification and Pose Estimation of Lamps for BIM Integration. Sensors 2018, 18, 2364. doi:10.3390/s18072364.
  32. Levenberg, K. A method for the solution of certain non-linear problems in least squares. Quart. J. Appl. Maths. 1944, II, 164–168.
  33. Marquardt, D.W. An Algorithm for Least-Squares Estimation of Nonlinear Parameters. SIAM J. Appl. Math. 1963, 11, 431–441.
  34. Marder-Eppstein, E. Project Tango. In ACM SIGGRAPH 2016 Real-Time Live!; ACM: New York, NY, USA, 2016; SIGGRAPH ’16, p. 25. doi:10.1145/2933540.2933550.
  35. OpenCV. Dr. Dobb’s J. Softw. Tools 2000, 120, 122–125.
  36. OpenMesh: A Generic and Efficient Polygon Mesh Data Structure. OpenSG Symposium 2002, 2002.
  37. Ceres Solver. Available online: http://ceres-solver.org (accessed on 27 March 2019).
  38. OpenGL Programming Guide: The Official Guide to Learning OpenGL, Version 4.3, 8th ed.; Addison-Wesley Professional: Boston, MA, USA, 2013.
  39. Rosin, P. Computing global shape measures. Handbook of Pattern Recognition and Computer Vision, 3rd ed.; World Scientific Publishing Company Inc.: Singapore, Singapore, 2005; pp. 177–196.
Citations (2)
View on Semantic Scholar

Summary

We haven't generated a summary for this paper yet.

Ai Sparkles Streamline Icon: https://streamlinehq.com Summarize Now
Dice Question Streamline Icon: https://streamlinehq.com

Open Problems

We haven't generated a list of open problems mentioned in this paper yet.

Ai Sparkles Streamline Icon: https://streamlinehq.com Generate Now
Lightbulb Streamline Icon: https://streamlinehq.com

Continue Learning

We haven't generated follow-up questions for this paper yet.

Ai Sparkles Streamline Icon: https://streamlinehq.com Generate Now
List To Do Tasks Checklist Streamline Icon: https://streamlinehq.com

Collections

Sign up for free to add this paper to one or more collections.

User Circle Single Streamline Icon: https://streamlinehq.com Sign Up