Skip to main content
Log in

Design of Smart Door Closer System with Image Classification over WLAN

  • Published:
Wireless Personal Communications Aims and scope Submit manuscript

Abstract

A dual purpose system is presented in this paper which serves not only as a door closer, but is equally effective for surveillance purposes. The currently deployed surveillance systems store a large amount of data, thereby consuming large memory spaces. The novel feature illustrated in this paper is that object identification and classification is performed for a desired area, along with controlled access. This system uses a neural network based learning algorithm before providing any instruction for the hardware. The system is innocuous except when object is identified in the surrounding area through motion detection and facial recognition techniques, thereby preventing large storage of unclassified video frames. In idle conditions, the system will work only as a surveillance system with object detection classifiers. The results can be stored on remote server for backup for security purpose.

This is a preview of subscription content, log in via an institution to check access.

Access this article

Price excludes VAT (USA)
Tax calculation will be finalised during checkout.

Instant access to the full article PDF.

Fig. 1
Fig. 2
Fig. 3
Fig. 4
Fig. 5
Fig. 6
Fig. 7
Fig. 8
Fig. 9

Similar content being viewed by others

References

  1. Shah, D., & haradi, V. (2016). IoT based biometrics implementation on Raspberry Pi. Procedia Computer Science, 79, 328–336. https://doi.org/10.1016/j.procs.2016.03.043.

    Article  Google Scholar 

  2. Hwang, R., Su, F., & Huang, L. (2006). Fast firmware implementation of RSA-like security protocol for mobile devices. Wireless Personal Communications, 42(2), 213–223. https://doi.org/10.1007/s11277-006-9174-y.

    Article  Google Scholar 

  3. Saravanan, P., & Kalpana, P. (2018). Novel reversible design of advanced encryption standard cryptographic algorithm for wireless sensor networks. Wireless Personal Communications, 100(4), 1427–1458. https://doi.org/10.1007/s11277-018-5647-z.

    Article  Google Scholar 

  4. Vujović, V., & Maksimović, M. (2015). Raspberry Pi as a sensor web node for home automation. Computers and Electrical Engineering, 44, 153–171. https://doi.org/10.1016/j.compeleceng.2015.01.019.

    Article  Google Scholar 

  5. Ransing, R., & Rajput, M. (2015). Smart home for elderly care, based on wireless sensor network. International Conference on Nascent Technologies in the Engineering Field (ICNTE). https://doi.org/10.1109/icnte.2015.7029932.

    Article  Google Scholar 

  6. Sruthy, S., & George, S. (2017). WiFi enabled home security surveillance system using Raspberry Pi and IoT module. In IEEE international conference on signal processing, informatics, communication and energy systems (SPICES). https://doi.org/10.1109/spices.2017.8091320

  7. Prajapati, U., Rawat, A., & Deb, D. (2018). A novel approach towards a low cost peripheral security system based on specific data rates. Wireless Personal Communications, 99(4), 1625–1637. https://doi.org/10.1007/s11277-018-5305-5.

    Article  Google Scholar 

  8. Rawat, A., Deb, D., Rawat, V., & Joshi, D. (2019). Methods and systems for data rate based peripheral security. No. 201721005324 A, Pub Date: February 24, 2017. India.

  9. Othman, N., & Aydin, I. (2017). A new IoT combined body detection of people by using computer vision for security application. In 9th international conference on computational intelligence and communication networks (CICN). https://doi.org/10.1109/cicn.2017.8319366

  10. Chen, Y., Chen, Q., Chou, K., & Wu, R. (2016). Low-cost face recognition system based on extended local binary pattern. International Automatic Control Conference (CACS). https://doi.org/10.1109/cacs.2016.7973876.

    Article  Google Scholar 

  11. Jara Ramos, G., Sanchez Garcia, J., & Ponomariov, V. (2015). Embedded system for real-time person detecting in infrared images/videos using super-resolution and Haar-like feature techniques. In 12th international conference on electrical engineering, computing science and automatic control (CCE). https://doi.org/10.1109/iceee.2015.7357980.

  12. Deng, J., Dong, W., Socher, R., Li, L., Li, Kai, & Fei-Fei, Li. (2009). ImageNet: A large-scale hierarchical image database. IEEE Conference on Computer Vision and Pattern Recognition. https://doi.org/10.1109/cvpr.2009.5206848.

    Article  Google Scholar 

  13. Everingham, M., Van Gool, L., Williams, C., Winn, J., & Zisserman, A. (2009). The pascal visual object classes (VOC) challenge. International Journal of Computer Vision, 88(2), 303–338. https://doi.org/10.1007/s11263-009-0275-4.

    Article  Google Scholar 

  14. Dollar, P., Wojek, C., Schiele, B., & Perona, P. (2012). Pedestrian detection: An evaluation of the state of the art. IEEE Transactions on Pattern Analysis and Machine Intelligence, 34(4), 743–761. https://doi.org/10.1109/tpami.2011.155.

    Article  Google Scholar 

  15. Lin, T., Maire, M., Belongie, S., Hays, J., Perona, P., Ramanan, D., et al. (2014). Microsoft COCO: Common objects in context. Computer Vision—ECCV, 2014, 740–755. https://doi.org/10.1007/978-3-319-10602-1-48.

    Article  Google Scholar 

  16. Deb, D., Rawat, A., Khanna, N., Agrawal, S., & Radadia, H. (2019). Smart imperceptible door closing systems with integrated monitoring devices. No. 201721021291 A, Pub Date: June 30, 2017. India.

Download references

Author information

Authors and Affiliations

Authors

Corresponding author

Correspondence to Jatin Upadhyay.

Additional information

Publisher's Note

Springer Nature remains neutral with regard to jurisdictional claims in published maps and institutional affiliations.

Rights and permissions

Reprints and permissions

About this article

Check for updates. Verify currency and authenticity via CrossMark

Cite this article

Upadhyay, J., Deb, D. & Rawat, A. Design of Smart Door Closer System with Image Classification over WLAN. Wireless Pers Commun 111, 1941–1953 (2020). https://doi.org/10.1007/s11277-019-06965-z

Download citation

  • Published:

  • Issue Date:

  • DOI: https://doi.org/10.1007/s11277-019-06965-z

Keywords

Navigation