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A Real-Time Method to Remotely Detect a Target Based on Color Average and Deviation

  • Henry CruzEmail author
  • Juan Meneses
  • Gustavo Andrade-Miranda
Conference paper
Part of the Communications in Computer and Information Science book series (CCIS, volume 895)

Abstract

This paper presents a new semiautomatic method to remotely segment a target in real-time. The aim is to obtain a fast distinction and detection based on RGB color space analysis. Firstly, a pixel of the desired target manually is selected and evaluated based on weighing different surrounding areas of interest (Ai). Later, statistical measures, identification of deviation parameters and the subsequent assignation of identifiers (ID’s) that are obtained from the color information of each region Ai. The performance of the algorithm is evaluated based on segmentation quality and computation time. These tests have been performed using databases as well in real-time and accessed in remote way (distance from the control-site 8.828.12 km) to prove the robustness of the algorithm. The results revealed that the proposed method performs efficiently in tasks as; objects detection in forested areas with high density (jungle images), segmentation in images with few color contrasts, segmentation in cases of partial occlusions, images with low light conditions and crowded scenes. Lastly, the results show a considerable decrease of the processing time and a more accurate detection of a specific target in relation with other methods proposed in literature.

Keywords

Segmentation Average color Identifiers Specific target Time processing 

Notes

Acknowledgments

Henry Cruz Carrillo gives thanks the Technological Scientific Research Center of the Ecuadorian Army (CICTE) for the collaboration obtained.

This work was sponsored by Spanish National Plan for Scientific and Technical Research and Innovation, project number TEC2013-48453-C2-2-R.

Henry Cruz Carrillo gives thanks Ecuadorian Air Force Research and Development Center (CIDFAE) for the collaboration obtained.

References

  1. 1.
    Xin, Z., Yee-Hong, Y., Zhiguang, H., Hui, W., Chao, G.: Object class detection: a survey. J. ACM Comput. Surv. (CSUR) 46(1), 101–151 (2013)Google Scholar
  2. 2.
    Tsai, M.K.: Automatically determining accidental falls in field surveying: a case study of integrating accelerometer determination and image recognition. Safety Sci. J. 66, 19–26 (2014)CrossRefGoogle Scholar
  3. 3.
    Galceran, E., Carreras, M.: A survey on coverage path planning for robotics. J. Robot. Auton. Syst. 61(12), 1258–1276 (2013)CrossRefGoogle Scholar
  4. 4.
    Cabrera, R., Tuytelaars, T.: Boosting masked dominant orientation templates for efficient object detection. Computer Vis. Image Und. J. 120, 103–116 (2014)CrossRefGoogle Scholar
  5. 5.
    Andrade-Miranda, G., Godino-Llorente, J.I.: Glottal gap tracking by a continuous background modeling using inpainting. Med. Biol. Eng. Comput. 55, 2123–2141 (2017)CrossRefGoogle Scholar
  6. 6.
    Dong, J., Xia, W., Chen, Q., et al.: Subcategory-aware object classification. In: IEEE Conference on Computer Vision and Pattern Recognition (CVPR), pp. 827−834 (2013)Google Scholar
  7. 7.
    Chia, A., et al.: Structural descriptors for category level object detection. IEEE Trans. Multimedia 11(8), 1407–1421 (2009)CrossRefGoogle Scholar
  8. 8.
    Richards, J., Xiuping, J.: Remote sensing Digital Image Analysis: An Introduction, 4th Edition, Chap. 8, pp. 193–338. Springer, Heidelberg (2005).  https://doi.org/10.1007/3-540-29711-1
  9. 9.
    Everingham, M., Van Gool, L., Williams, C., Winn, J., Zisserman, A.: The PASCAL visual object classes (VOC) challenge. Int. J. Comput. Vis. 88(2), 303–338 (2010)CrossRefGoogle Scholar
  10. 10.
    Maggio, E., Cavallaro, A.: Video Tracking Theory and Practice, 3rd edn, pp. 3–120. Wiley, Hoboken (2011)CrossRefGoogle Scholar
  11. 11.
    Lei, F., Fergus, R., Perona, P.: Learning generative visual models from few training examples: an incremental Bayesian approach tested on 101 object categories. Computer Vis. Image Underst. 106(1), 59–70 (2007)CrossRefGoogle Scholar
  12. 12.
    Pérez, P., Hue, C., Vermaak, J., Gangnet, M.: Color based in probabilistic tracking. In: Proceedings of 7th European Conference Computer Vision, pp. 661–675 (2002)Google Scholar
  13. 13.
    Ross, D., Lim, J., Lin, R., Yang, M.: Incremental learning for robust visual tracking. Int. J. Comput. Vis. 77, 125–141 (2008)CrossRefGoogle Scholar
  14. 14.
    Avidan, S.: Ensemble tracking. IEEE Trans. Pattern Anal. Mach. Intell. 29(2), 261–271 (2007)CrossRefGoogle Scholar
  15. 15.
    Wang, S., Lu, H., Yang, F., Yang, M.: Superpixel tracking. In: Proceedings of IEEE International Conference Computer Vision, 1323–1330 (2011)Google Scholar
  16. 16.
    Grabner, H., Bischof, H.: On-line boosting and vision. In: Proceedings of IEEE Conference on Computer Vision and Pattern Recognition, pp. 260–267 (2006).  https://doi.org/10.1109/cvpr.2006.215
  17. 17.
    Lui, Z., Zou, W., Le Meur, O.: Saliency tree: a novel saliency detection framework. IEEE Trans. Image Process. 23(5), 1932–1952 (2014)MathSciNetzbMATHGoogle Scholar
  18. 18.
    Seo, Y., Lee, K.: Category classification of multispectral image data using spatial information in the small image region. IEEE Geosci. Remote Sens. Symp. 4, 1978–1980 (1993)Google Scholar
  19. 19.
    Jianghong, S., Zhongming, Z., Qingye, Z., Yanfeg, W.: An algorithm for eliminating the isolated regions based on connected area in image classification. IEEE Geosci. Remote Sens. Symp. 5, 3058–3061 (2004)Google Scholar
  20. 20.
    Martin, D., Fowlkes, C., Tal, D., et al.: A database of human segmented natural images and its applications to evaluating segmentation algorithms and measuring ecological statistics. In: Proceedings of International Conference Computer Vision, Vancouver, pp. 416–425 (2001)Google Scholar
  21. 21.
    Crevier, D.: Image segmentation algorithm development using ground truth image data sets. Comput. Vis. Image Understand. 112(2), 143–159 (2008).  https://doi.org/10.1016/j.cviu.2008.02.002CrossRefGoogle Scholar
  22. 22.
    Wang, M., Li, R.: Segmentation of high spatial resolution remote sensing imagery based on hard-boundary constraint and two-stage merging. IEEE Trans. Geosci. Remote Sens. 52(9), 5712–5725 (2014)CrossRefGoogle Scholar
  23. 23.
    Polak, M., Zhang, H., Pi, M.: An evaluation metric for image segmentation of multiple objects. J. Image Vis. Comput. 27(8), 1223–1227 (2009)CrossRefGoogle Scholar
  24. 24.
    Herwitz, S.R., et al.: Imaging from an unmanned aerial vehicle: Agri- cultural surveillance and decision support. Comput. Electron. Agricult. 44(1), 49–61 (2004)CrossRefGoogle Scholar
  25. 25.
    Chan, T.F., Vese, L.A.: Active contours without edges. IEEE Trans. Image Process. 10(2), 266–277 (2001)CrossRefGoogle Scholar
  26. 26.
    Cruz, H., Eckert, M., Meneses, J., Martínez, J.F.: Precise real-time detection of nonforested areas with UAVs. IEEE Trans. Geosci. Remote Sens. 55(2), 632–644 (2017)CrossRefGoogle Scholar
  27. 27.
    Cruz, H., Eckert, M., Meneses, J., Martínez, J.F.: Efficient forest fire detection index for application in unmanned aerial systems (UASs). Sensors 16(6), 893, 1–15 (2016).  https://doi.org/10.3390/s16060893CrossRefGoogle Scholar

Copyright information

© Springer Nature Switzerland AG 2019

Authors and Affiliations

  • Henry Cruz
    • 1
    Email author
  • Juan Meneses
    • 2
  • Gustavo Andrade-Miranda
    • 3
  1. 1.Universidad de las Fuerzas Armadas-ESPESangolquíEcuador
  2. 2.Research Center on Software Technologies and Multimedia Systems for Sustainability (CITSEM)Technical University of MadridMadridSpain
  3. 3.Facultad de Ingeniería Industrial. Av. Las AguasUniversidad de GuayaquilGuayaquilEcuador

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