Baggage Detection and Recognition Using Local Tri-Directional Pattern

Year : 2023 | Volume :01 | Issue : 01 | Page : 08-17
By

Ayesha

Jahanzaib Ali Khan

Waqas Ahmad

Muhammad Nadeem

Syeda Wajiha Zahra

Ali Arshad

Saman Riaz

Usman Shahid

  1. Lecturer Department of Computer Science, University of Engineering and Technology Taxila Pakistan
  2. Lecturer Department of Computer Science and Software Engineering, International Islamic University Islamabad Pakistan
  3. Lecturer Department of Computer Science, Alhamd Islamic University Islamabad Pakistan
  4. Lecturer Department of Computer Science, Alhamd Islamic University Islamabad Pakistan
  5. Lecturer Department of Computer Science, Abasyn University Islamabad Pakistan
  6. Lecturer Department of Computer Science, National University of Technology Islamabad Pakistan
  7. Lecturer Department of Computer Science, National University of Technology Islamabad Pakistan
  8. Lecturer Department of Computer Science, Iqra University Islamabad Pakistan

Abstract

Nowadays, pattern-based image retrieval algorithms are gaining popularity just because of their uniqueness. There are several issues in the previously proposed systems. The proposed system resolves issues highlighted in the literature. Our proposed system is tested on two image datasets ILIDS and PETS 2006. LTDP provides good results as compared to LBP in baggage detection on two classes that either bag is present or not in an image because LTDP works on finding the difference between adjacent neighbors and magnitude pattern which is either 0 or 1 which means either bag is present or not. In addition to LTDP patterns, HOG transformation has also been used for better feature extraction results. The results obtained through ANN are 90% whereas SVM depicts 50% accuracy; and through classification learner, 75% accuracy is obtained.

Keywords: LTDP, LBP, baggage detection, pattern-based image retrieval algorithms, baggage detection

[This article belongs to International Journal of Mobile Computing Technology(ijmct)]

How to cite this article: Ayesha, Jahanzaib Ali Khan, Waqas Ahmad, Muhammad Nadeem, Syeda Wajiha Zahra, Ali Arshad, Saman Riaz, Usman Shahid. Baggage Detection and Recognition Using Local Tri-Directional Pattern. International Journal of Mobile Computing Technology. 2023; 01(01):08-17.
How to cite this URL: Ayesha, Jahanzaib Ali Khan, Waqas Ahmad, Muhammad Nadeem, Syeda Wajiha Zahra, Ali Arshad, Saman Riaz, Usman Shahid. Baggage Detection and Recognition Using Local Tri-Directional Pattern. International Journal of Mobile Computing Technology. 2023; 01(01):08-17. Available from: https://journals.stmjournals.com/ijmct/article=2023/view=112949


Browse Figures

References

  1. Khanam T, Deb K. Baggage Recognition in Occluded Environment using Boosting Technique. KSII Trans Internet Inf Syst (TIIS). 2017; 11(11): 5436–5458.
  2. Khanam T, Deb K. Human and carried baggage detection & classification based on RSD-HOG in video frame. In 2016 9th International Conference on Electrical and Computer Engineering (ICECE). 2016; 415–418.
  3. Leone K, Liu RR. The key design parameters of checked baggage security screening systems in airports. J Air Transp Manag. 2005; 11(2): 69–78.
  4. Nadeem Muhammad, Arshad Ali, Riaz Saman, Zahra Syedawajiha, Rashid Muhammad, Band Shahab S, Mosavi Amir. Preventing Cloud Network from Spamming Attacks Using Cloudflare and KNN. Comput Mater Contin. 2022; 74(2): 2641–2659. 10.32604/cmc.2023.028796.
  5. Smith K, Quelhas P, Gatica-Perez D. Detecting abandoned luggage items in a public space. In IEEE Performance Evaluation of Tracking and Surveillance Workshop (PETS). 2006; 75–82.
  6. Chang J-Y, Liao H-H, Chen L-G. Localized detection of abandoned luggage. EURASIP J Adv Signal Process. 2010; 2010: 675784.
  7. Tzanidou G, Zafar I, Edirisinghe EA. Carried object detection in videos using color information. IEEE Trans Inf Forensics Secur. 2013; 8(10): 1620–1631.
  8. Beleznai C, Gemeiner P, Zinner C. Reliable left luggage detection using stereo depth and intensity cues. In Proceedings of the IEEE International Conference on Computer Vision Workshops. 2013; 59–66.
  9. Lin K, Chen S-C, Chen C-S, Lin D-T, Hung Y-P. Left-luggage detection from finite-state-machine analysis in static-camera videos. In 2014 22nd International Conference on Pattern Recognition. 2014; 4600–4605.
  10. Zahra SW, Arshad A, Nadeem M, Riaz S, Dutta AK, Alzaid Z, Alabdan R, Almutairi B, Almotairi S. Development of Security Rules and Mechanisms to Protect Data from Assaults. Appl Sci. 2022; 12(24): 12578. [online] doi:10.3390/app122412578.
  11. Wahyono, Jo KH. Detecting and recognizing LED dot matrix text in natural scene images. InEmerging Intelligent Computing Technology and Applications: 9th International Conference, ICIC 2013, Nanning, China, July 28-31, 2013. Proceedings 9 2013 (pp. 153–158). Springer Berlin Heidelberg.
  12. Johnstone M, Creighton D, Nahavandi S. Simulation-based baggage handling system merge analysis. Simul Model Pract Theory. 2015; 53: 45–59.
  13. Nadeem Muhammad, Arshad Ali, Riaz Saman, Zahra Syeda, Dutta Ashit, Alaybani Sultan. Preventing the Cloud Networks through Semi-Supervised Clustering from Both Sides Attacks. Appl Sci. 2022; 12(15): 7701(19p). 10.3390/app12157701.
  14. Jo K-H. Carried baggage detection and classification using part-based model. In International Conference on Intelligent Computing. 2015; 289–296.
  15. Szwoch G. Extraction of stable foreground image regions for unattended luggage detection. Multimed Tools Appl. 2016; 75(2): 761–786.
  16. Hättenschwiler N, Sterchi Y, Mendes M, Schwaninger A. Automation in airport security X-ray screening of cabin baggage: Examining benefits and possible implementations of automated explosives detection. Appl Ergon. 2018; 72: 58–68.
  17. Wang C, Liu J, Chen Y, Liu H, Wang Y. Towards In-baggage Suspicious Object Detection Using Commodity Wi-Fi. In 2018 IEEE Conference on Communications and Network Security (CNS). 2018; 1–9.
  18. Smeureanu S, Ionescu RT. Real-time deep learning method for abandoned luggage detection in video. In 2018 26th European Signal Processing Conference (EUSIPCO). 2018; 1775–1779.
  19. Nadeem Muhammad, Arshad Ali, Riaz Saman, Shahab Band S, Mosavi Amir. Intercept the Cloud Network from Brute Force and DDoS Attacks via Intrusion Detection and Prevention System. IEEE Access. 2021; 9: 152300–152309. 10.1109/ACCESS.2021.3126535.
Regular Issue Subscription Review Article
Volume 01
Issue 01
Received June 1, 2023
Accepted June 2, 2023
Published January 20, 2023
Views: 903