DOI - Vydavatelství Mendelovy univerzity v Brně

Identifikátory DOI

DOI: 10.11118/978-80-7509-990-7-0159

MOBILE AUGMENTED REALITY OBJECT DETECTION APPLICATION

Jan Strnad1, Jaromír Landa1
1 Computer science department, Faculty of Business and Economics, Mendel University in Brno, Zemědělská 1, 613 00 Brno, Czech Republic

This article proposes a Mobile Augmented Reality (MAR) application for object detection. The application can detect predefined objects in the camera stream and display infor- mation about them. Object detection poses many challenges, and a common approach is to perform it remotely on a server. However, this requires an active internet connection. Alternatively,detection can be performed locally using a model stored on the device.How- ever, not all devices have the capability to perform real-time detection. We have created a Mobile Augmented Reality app that can detect objects in the camera stream. The app can perform detection locally or remotely, depending on the device’s configuration. Sec- ondly, the app’s ability to perform detection locally or remotely makes it versatile. The paper has two main contributions. Firstly, the proposed application architecture can be applied to any similar MAR app. The application was tested on multiple Android devices to determine the minimum configuration required for local object detection.

Klíčová slova: object detection, Mobile Augmented Reality, Tensor Flow

stránky: 159-166, online: 2024



Reference

  1. APICHARTTRISORN, K., RAN, X., CHEN, J., KRISHNAMURTHY, S. V. and ROY-CHOW-DHURY, A. K. 2019. Frugal following: power thrifty object detection and tracking for mobile augmented reality. In: Proceedings of the 17th Conference on Embedded Net- worked Sensor Systems (SenSys '19). Association for Computing Machinery, New York, NY, USA, 96-109. https://doi.org/10.1145/3356250.3360044 Přejít k původnímu zdroji...
  2. AZZO, F., TAQI, A. M. and MILANOVA, M. 2018. Human Related-Health Actions Detection using Android Camera based on TensorFlow yObject Detection API. International Journal of Advanced Computer Science and Applications, 9(10). https://doi.org/10.14569/IJACSA.2018.091002 Přejít k původnímu zdroji...
  3. CAI, Y., Li, H., YUAN, G., NIU, W., Li, Y., TANG, X., REN, B. and WANG, Y. 2020. YOLObile: Real-Time Object Detection on Mobile Devices via Compression-Compilation CoDesign. arXiv. https://doi.org/10.48550/arXiv.2009.05697 Přejít k původnímu zdroji...
  4. GHASEMI, Y., JEONG, H. CHOI, S. H., PARK, K. and LEE, J. Y. 2022. Deep learning-based object detection in augmented reality: A systematic review. Computers in Industry, 139, 103661. ISSN 0166-3615. https://doi.org/10.1016/j.compind.2022.103661 Přejít k původnímu zdroji...
  5. CHILUKURI, D. M., YI, S. and SEONG, Y. A robust object detection system with occlusion han- dling for mobile devices. Computational Intelligence, 38(4): 1338-1364. https://doi.org/10.1111/coin.12511 Přejít k původnímu zdroji...
  6. LI, X., QIN, Y., LIU, Z., ZOMAYA, A. and LIAO, X. 2022. Towards efficient and robust intelligent mobile vision system via small object aware parallel offloading. Journal of Systems Architecture, 129, 102595.ISSN 1383-7621. https://doi.org/10.1016/j.sysarc.2022.102595 Přejít k původnímu zdroji...
  7. LIU, Q. and HAN, T. 2018. DARE: Dynamic Adaptive Mobile Augmented Reality with Edge Computing. In: IEEE 26th International Conference on Network Protocols (ICNP). Cambridge, UK, 2018, pp. 1-11. https://doi.org/10.1109/ICNP.2018.00011 Přejít k původnímu zdroji...
  8. LIU, L., LI, H. and GRUTESER, M. 2019. Edge Assisted Real-time Object Detection for Mo- bile Augmented Reality. In: The 25th Annual International Conference on Mobile Computing and Networking (MobiCom '19). Association for Computing Machinery, New York, NY, USA, Article 25, 1-16. https://doi.org/10.1145/3300061.3300116 Přejít k původnímu zdroji...
  9. KNEZ, S. and ŠAJN, L. 2020. Food object recognition using a mobile device: Evaluation of currently implemented systems. Trends in Food Science & Technology, 99, 460-471. ISSN 0924-2244. http://doi.org/10.1016/j.tifs.2020.03.017 Přejít k původnímu zdroji...
  10. MARTINEZ-ALPISTE, I. et al. 2022. Smartphone-based real-time object recognition archi- tecture for portable and constrained systems. J Real-Time Image Proc., 19. https://doi.org/10.1007/s11554-021-01164-1 Přejít k původnímu zdroji...
  11. SAVCHENKO, A. V., DEMOCHKIN, K. V. and GRECHIKHIN, I. S. 2022. Preference prediction based on a photo gallery analysis with scene recognition and object detection. Pattern Recognition, 121, 108248. ISSN 0031-3203. https://doi.org/10.1016/j.patcog.2021.108248 Přejít k původnímu zdroji...
  12. THIEN, H., PHAM, Q., PHAM, X., NGUYEN, T. T., HAN, Z. and KIM, D. 2023. Artificial intelligence for the metaverse: A survey. Engineering Applications of Artificial Intelligence, 117(Part A), 105581. ISSN 0952-1976. https://doi.org/10.1016/j.en- gappai.2022.105581 Přejít k původnímu zdroji...
  13. WANG, X., YANG, Z., WU, J., ZHAO, Y. and ZHOU, Z. 2021. EdgeDuet: Tiling Small Object Detection for Edge-Assisted Autonomous Mobile Vision. In: IEEE INFOCOM 2021 - IEEE Conference on Computer Communications. Vancouver, BC, Canada, pp. 1-10. https://doi.org/10.1109/INFOCOM42981.2021.9488843 Přejít k původnímu zdroji...
  14. XIONG, Y. et al. 2021. MobileDets: Searching for Object Detection Architectures for Mo- bile Accelerators. In: IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR), pp. 3824-3833. https://doi.org/10.1109/CVPR46437.2021.00382 Přejít k původnímu zdroji...
  15. ZHOU, X. and ZHAO, J. 2022. Mobile Augmented Reality with Federated Learning in the Metaverse. 2022. arXiv. https://doi.org/10.48550/arXiv.2212.08324 Přejít k původnímu zdroji...