STM Journals Peer-reviewed research from Consortium e-Learning Network

International Journal of Mobile Computing Technology Review Article

Baggage Detection and Recognition Using Local Tri-Directional Pattern

  1. Ayesha Department of Computer Science, University of Engineering and Technology
  2. Ali Khan Department of Computer Science and Software Engineering, International Islamic University
  3. Muhammad Nadeem Department of Computer Science, Alhamd Islamic University
  4. Syeda Wajiha Zahra Department of Computer Science, Abasyn University
  5. Ali Arshad Department of Computer Science, National University of Technology
  6. Saman Riaz Department of Computer Science, National University of Technology
  7. Usman Shahid Department of Computer Science, Iqra University

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

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