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A Dynamic Object Detection In Real-World Scenarios

  • Kausar Hena
  • J. AmudhaEmail author
  • R. Aarthi
Conference paper
Part of the Lecture Notes on Data Engineering and Communications Technologies book series (LNDECT, volume 28)

Abstract

The object recognition is one of the most challenging tasks in computer vision, especially in the case of real-time robotic object recognition scenes where it is difficult to predefine an object and its location. To address this challenge, we propose an object detection method that can be adaptive to learn objects independent of the environment, by enhancing the relevant features of the object and by suppressing the other irrelevant feature. The proposed method has been modeled to learn the association of features from the given training dataset. Using dynamic evolution of neuro-fuzzy inference system (DENFIS) model has been used to generate number of rules from the cluster formed from the dataset. The validation of the model has been carried on various datasets created from the real-world scenario. The system is capable of locating the target regardless of scale, illumination variance, and background.

Keywords

Computer vision Fuzzy system Region of interest 

References

  1. 1.
    Sampath A, Sivaramakrishnan A, Narayan K, Aarthi R (2016) A study of household object recognition using SIFT-based bag-of-words dictionary and SVMs. In: Proceedings of the international conference on soft computing systems. Springer, New Delhi, pp 573–580Google Scholar
  2. 2.
    Lowe DG (1999) Object recognition from local scale-invariant features. In: The proceedings of the seventh IEEE international conference on computer vision, vol 2, IEEE, pp 1150–1157Google Scholar
  3. 3.
    Kaur A, Kranthi BV (2012) Comparison between YCbCr color space and CIELab color space for skin color segmentation. IJAIS 3(4):30–33Google Scholar
  4. 4.
    Alpaydin E (2014) Introduction to machine learning. MIT press, Dec 4Google Scholar
  5. 5.
    Li G, Yu Y (2016) Deep contrast learning for salient object detection. In Proceedings of the IEEE conference on computer vision and pattern recognition, pp 478–487Google Scholar
  6. 6.
    Schneiderman H, Kanade T (2000) A statistical method for 3D object detection applied to faces and cars. In: Proceedings IEEE conference on computer vision and pattern recognition, vol 1. IEEE, pp 746–751Google Scholar
  7. 7.
    Viola P, Jones M (2001) Robust real-time face detection. In: Proceedings. Eighth IEEE international conference on computer vision. ICCV, 2001. IEEE, p 747Google Scholar
  8. 8.
    Mohan A, Papageorgiou C, Poggio T (2001) Example-based object detection in images by components. IEEE Trans Pattern Anal Mach Intell 23(4):349–361CrossRefGoogle Scholar
  9. 9.
    Serge B, Malik J, Puzicha J (2002) Shape matching and object recognition using shape contexts. IEEE Trans Pattern Anal Mach Intell 24(4):509–522Google Scholar
  10. 10.
    Torralba A (2003) A Contextual priming for object detection. Int J Comput Vis 53(2):169–191Google Scholar
  11. 11.
    Torralba A, Murphy K, Freeman W, Rubin M (2003) Context-based vision system for place and object recognition. In: IEEE international conference on computer vision (ICCV), pp 273–280Google Scholar
  12. 12.
    Ma Y, Hua X, Lu L, Zhang H (2005) A generic framework of user attention model and its application in video summarization. IEEE Trans Multimed 7(5):907–919Google Scholar
  13. 13.
    Itti L (2004) Automatic foveation for video compression using a neurobiological model of visual attention. IEEE Trans Image Process 13(10):1304–1318CrossRefGoogle Scholar
  14. 14.
    Siagian C, Itti L (2009) Biologically inspired mobile robot vision localization. IEEE Trans Robot 25(4):861–873CrossRefGoogle Scholar
  15. 15.
    Mertsching B, Bollmann M, Hoischen R, Schmalz S (1999) The neural active vision system. In: Jahne B, Haussecke H, Geissler P (eds) Handbook of computer vision and applications vol 3. Academic Press, pp 543–568Google Scholar
  16. 16.
    Siagian C, Chang CK, Itti L (2014) Autonomous mobile robot localization and navigation using a hierarchical map representation primarily guided by vision. J Field Robot 31(3):408–440CrossRefGoogle Scholar
  17. 17.
    Pasquale G, Ciliberto C, Odone F, Rosasco L, Natale L (2015) Teaching iCub to recognize objects using deep convolution neural networks. In: Proceedings of The 4th workshop on machine learning for interactive systems at ICML 2015, PMLR, vol 43, pp 21–25Google Scholar
  18. 18.
    Lin W-S, Fang C-H (2006) Computational model of intention-oriented visual attention. In: IEEE international conference on systems, man, and cybernetics October pp 8–11Google Scholar
  19. 19.
    Kasabov NK, Song Q (2002) DENFIS: dynamic evolving neural-fuzzy inference system and its application for time-series prediction. IEEE Trans Fuzzy Syst 10(2):144–154CrossRefGoogle Scholar
  20. 20.
    Amudha J, Radha D, Smitha S (2015) Analysis of fuzzy rule optimization models. Int J Eng Technol (IJET) 7:1564–1570Google Scholar
  21. 21.
    Frintrop S (2006) VOCUS.: a visual attention system for object detection and goal-directed search, vol 3899, Lecture Notes in Artificial Intelligence (LNAI). Springer, Heidelberg, GermanyCrossRefGoogle Scholar
  22. 22.
    Fadhilah R (2016) Fuzzy petri nets as a classification method for automatic speech intelligibility detection of children with speech impairments/Fadhilah Rosdi. Diss. University of MalayaGoogle Scholar
  23. 23.
    Bruce N, Tsotsos J (2006) Saliency based on information maximization. In: Advances in neural information processing systems, pp 155–162Google Scholar

Copyright information

© Springer Nature Singapore Pte Ltd. 2019

Authors and Affiliations

  1. 1.Department of Computer Science and Engineering, Amrita School of EngineeringAmrita Vishwa VidyapeethamBengaluruIndia
  2. 2.Department of Computer Science and Engineering, Amrita School of EngineeringAmrita Vishwa VidyapeethamCoimbatoreIndia

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