KSII Transactions on Internet and Information Systems (TIIS)

Korean Society for Internet Information (한국인터넷정보학회)
권호 표지
DOI QR코드

DOI QR Code

Baggage Recognition in Occluded Environment using Boosting Technique

  • Khanam, Tahmina (Department of Computer Science & Engineering, Chittagong University of Engineering & Technology (CUET)) ;
  • Deb, Kaushik (Department of Computer Science & Engineering, Chittagong University of Engineering & Technology (CUET))
  • Received : 2017.04.28
  • Accepted : 2017.07.28
  • Published : 2017.11.30
Download PDF
⟨ Previous Next ⟩

Abstract

Automatic Video Surveillance System (AVSS) has become important to computer vision researchers as crime has increased in the twenty-first century. As a new branch of AVSS, baggage detection has a wide area of security applications. Some of them are, detecting baggage in baggage restricted super shop, detecting unclaimed baggage in public space etc. However, in this paper, a detection & classification framework of baggage is proposed. Initially, background subtraction is performed instead of sliding window approach to speed up the system and HSI model is used to deal with different illumination conditions. Then, a model is introduced to overcome shadow effect. Then, occlusion of objects is detected using proposed mirroring algorithm to track individual objects. Extraction of rotational signal descriptor (SP-RSD-HOG) with support plane from Region of Interest (ROI) add rotation invariance nature in HOG. Finally, dynamic human body parameter setting approach enables the system to detect & classify single or multiple pieces of carried baggage even if some portions of human are absent. In baggage detection, a strong classifier is generated by boosting similarity measure based multi layer Support Vector Machine (SVM)s into HOG based SVM. This boosting technique has been used to deal with various texture patterns of baggage. Experimental results have discovered the system satisfactorily accurate and faster comparative to other alternatives.

Keywords

References

  1. Wahyono, J. Haariyono, and K.H. Jo, "Body part boosting model for carried baggage detection and classification," Neurocomputing, vol. 228, pp. 106-118, March 2017. https://doi.org/10.1016/j.neucom.2016.10.038
  2. G. Tzanidou and E. A. Edirisinghe, "Automatic baggage detection and classification," in Proc. of IEEE International Conference on Intelligent Systems Design and Applications, pp. 825-830, 2011.
  3. D. Venkatrayappa, P. Montesinos, D. Diep and B. Magnier, "RSD-HoG: a new image descriptor," ser. Lecture Notes on Computer Science, Springer International Publishing Switzerland, vol. 9127, 2015.
  4. V. D. Hoang, M. H. Le and K.H. Jo, "Hybrid cascade boosting machine using variant scale blocks based HOG features for pedestrian detection," Neurocomputing, vol. 135, pp. 357-366, 2014. https://doi.org/10.1016/j.neucom.2013.12.017
  5. S. Nigam, K. Deb and A. Khare, "Moment invariants based object recognition for different pose and appearances in real scenes," in Proc. IEEE International Conference on Informatics, Electronics & Vision, pp. 1-5, 2013.
  6. C. Zhao, C. Liu and Z. Lai, "Multiscale GIST feature manifold for building recognition," Neurocomputing, vol. 74, pp. 2929-2940, 2011. https://doi.org/10.1016/j.neucom.2011.03.035
  7. N. Dalal and B. Triggs, "Histograms of oriented gradients for human detection," IEEE Computer Society, vol. 1, pp. 886-893, 2005.
  8. D.G. Lowe, "Distinctive image features from scale-invariant keypoints," International Journal of Computer Vision, vol. 60, pp. 91-110, 2004. https://doi.org/10.1023/B:VISI.0000029664.99615.94
  9. P. Abeles, "Speeding up SURF," ser. Lecture Notes on Computer Science, Springer-Verlag Berlin Heidelberg, vol. 8034, 2013.
  10. W. Bingjian, L. Yapeng, L. Quan, F. Li, L. Qing, Q. Hanlin, Z. Huixin, and L. Shangqian, "Image registration algorithm based on modified GLOH descriptor for infrared images and electro-optical images," ser. Lecture Notes in Electrical Engineering, Springer-Verlag Berlin Heidelberg, vol. 128, 2012.
  11. P. Geismann and A. Knoll, "Speeding up HOG and LBP features for pedestrian detection by multiresolution techniques," Advances in Visual Computing, pp. 243-252, 2010.
  12. J. Shen, W. Yang and C. Sun, "Real-time human detection based on gentle MILBoost with variable granularity HOG-CSLBP," Neural Computing and Applications, vol.23, pp.1937-1948, 2013. https://doi.org/10.1007/s00521-012-1153-5
  13. M. K. Islam, F. Jahan, and J. H. Baek, "Object cataloging using heterogeneous local features for image retrieval," KSII Transactions on Internet and Information Systems, vol. 9, no. 11, pp. 4534-4555, November 2015. https://doi.org/10.3837/tiis.2015.11.016
  14. G. Zhao and M. Pietikainen, "Dynamic texture recognition using local binary patterns with an application to facial expressions," IEEE Transactions on Pattern Analysis and Machine Intelligence, vol.29, pp. 915-928, 2007. https://doi.org/10.1109/TPAMI.2007.1110
  15. M. Heikkila and M. Pietikainen, "A texture-based method for modeling the background and detecting moving objects," IEEE Transactions on Pattern Analysis and Machine Intelligence, vol.28, pp. 657-662, 2006. https://doi.org/10.1109/TPAMI.2006.68
  16. K. Deb, S. Imtiaz and P. Biswas, "A motion region detection and tracking method," Smart Computing Review, vol. 4, no. 1, pp. 79-90, 2014.
  17. K. Deb and A.H. Sunny, "Shadow detection and removal based on YCbCr color space," Smart Computing Review, vol. 4, no. 1, pp. 23-33, 2014.
  18. S. F. Tom, Haines and T. Xiang, "Background subtraction with dirichlet process mixture models," IEEE Transactions on Pattern Analysis and Machine Intelligence, vol. 36, no. 4, pp. 670-683, 2014. https://doi.org/10.1109/TPAMI.2013.239
  19. B. V. Vishnyakov, S. V. Sidyakin and Y. V. Vizilter, "Diffusion background model for moving object detection," The International Archives of the Photogrammetry, Remote Sensing and Spatial Information Sciences, vol. XL-5/W6, 2015.
  20. S. Nigam and A. Khare, "Integration of moment invariants and uniform local binary patterns for human activity recognition in video sequences," Multimedia Tools and Applications, vol. 75, no. 24, pp. 17303-17332, December 2016. https://doi.org/10.1007/s11042-015-3000-z
  21. S. H. Anamandra and V. Chandrasekaran, "COLOR CHILD: a novel color image local descriptor for texture classification and segmentation," Pattern Anal Applic, Springer-Verlag London, 2015.
  22. T. Khanam and K. Deb, "Human and carried baggage detection & classification based on RSD-HOG in video frame," in Proc. IEEE International Conference on Electrical and Computer Engineering, pp. 415-418, 2016.
  23. W. M. S. Arnold, M. C. Dung, C. Rita, C. Simone, D. Afshin and S. Mubarak, "Visual Tracking: an experimental survey," IEEE Transactions on Pattern Analysis and Machine Intelligence, vol. 36, no. 7, pp. 1442-1468, July 2014. https://doi.org/10.1109/TPAMI.2013.230
  24. K. Khoshelham, C. Nardinocchi, E. Frontoni, A. Mancini and P. Zingaretti, "Performance evaluation of automated approaches to building detection in multi-source aerial data," ISPRS Journal of Photogrammetry and Remote Sensing, vol. 65, pp. 123-133, 2010. https://doi.org/10.1016/j.isprsjprs.2009.09.005
  25. J. Chen, S. Shan, C. He, G. Zhao, M. Pietikainen, X. Chen and W. Gao, "WLD: a robust local image descriptor," IEEE Transactions on Pattern Analysis and Machine Intelligence, vol. 32, no. 9, pp. 1705-1720, 2010. https://doi.org/10.1109/TPAMI.2009.155

Cited by

  1. A method of generating virtual shadow dataset of buildings for the shadow detection and removal vol.21, pp.5, 2017, https://doi.org/10.7472/jksii.2020.21.5.49