A trusted waterfall framework based moving object detection using FACO-MKFCM techniques

  • T. MahalingamEmail author
  • M. Subramoniam


Object detection and tracking are necessary and challenging activities in several pc visual scene applications such as security, motor vehicle navigating and independent robotic navigating. Video scenery security in an energetic atmosphere, particularly for people and motor vehicles, is just one of the present difficult research study subjects in computer system vision. It is a vital modern technology to attack in opposition to violence, criminal offense and publicised security as well as for effective administration of heavy traffic. In this area, monitoring of motionless forefront objects is just one of the most essential needs for security systems based upon the monitoring of deserted or taken objects or parked motor vehicles. It is really challenging for the existing approach to efficiently identify the objects as they occur. In this research suggested an effective method, Fuzzy based Ant Colony Optimization (FACO) integrated with modified kernel fuzzy c-means algorithm (MKFCM) for object segmentation. Here FACO algorithm situates optimal initial cluster centroid for the MKFCM, thus improve all applications affiliated fuzzy clustering such as foreground segmentation in image processing. The recommended new method is intellectual and additionally dynamic clustering technique for the dividing of motion objects. After the segmentation, the object tracking can be done using particle filtering method. The morphological operation helps the particle filter to effectively track the objects. Here the flow is based on the traditional waterfall approach. From the empirical impacts, the recommended method outshines than the state of artwork. Below the suggested method attains maximum efficiency for both PETS and also Hall monitor videos when examined to the current algorithm.


Modified kernel fuzzy c-means Ant Colony Optimization (ACO) Fuzzy logic (FL) Foreground Background Clustering 



  1. 1.
    Agarwal P, Kumar S, Singh R, Agarwal P, Bhattacharya M (2015) A combination of bias-field corrected fuzzy c-means and level set approach for brain MRI image segmentation. IEEE international conference on soft computing and machine intelligence, pp 84–87Google Scholar
  2. 2.
    Ahmed MN, Yamany SM, Mohamed N, Farag AA, Moriarty T (2002) A modified fuzzy c-means algorithm for bias field estimation and segmentation of MRI data. IEEE Trans Med Imaging 21(3)Google Scholar
  3. 3.
    Ajala Funmilola A, Oke OA, Adedeji TO, Alade OM, Adewusi EA (2012) Fuzzy K-C-means clustering algorithm for medical image segmentation. Journal of Information Engineering and Applications 2(6)Google Scholar
  4. 4.
    Allin Christe1 S, Malathy K, Kandaswamy A (2010) Improved hybrid segmentation of brain MRI tissue and tumor using statistical features. ICTACT Journal on Image and Video Processing 1(1)Google Scholar
  5. 5.
    Azab MM, Shedeed HA, Hussein AS (2014) New technique for online object tracking-by-detection in video. IET Image Process 8(12):794–803CrossRefGoogle Scholar
  6. 6.
    Bezdek JC, Pal MR, Keller J, Krisnapuram R (1999) Fuzzy models and algorithms for pattern recognition and image processing. Kluwer Academic Publishers, NorwellCrossRefzbMATHGoogle Scholar
  7. 7.
    Bhaskar PK, Yong S-P (2014) Image processing based vehicle detection and tracking method. International conference on computer and information sciences (ICCOINS), Malaysia, pp 1–5Google Scholar
  8. 8.
    Bhoyar K, Kakde O (2010) Color image segmentation based on JND color histogram. International Journal of Image Processing (IJIP) 3(6):282–293Google Scholar
  9. 9.
    Bouguessa M, Wang S, Sunb H (2006) An objective approaches to cluster validation. Pattern Recogn Lett 27(13):1419–1430CrossRefGoogle Scholar
  10. 10.
    Capitaine HL, Frélicot C (2011) A fast fuzzy c-means algorithm for color image segmentation. In: Proceedings of European society for Fuzzy Logic and technology (EUSFLAT), pp 1074–1081Google Scholar
  11. 11.
    Cheng FH, Chen YL (2006) Real time multiple objects tracking and identification based on discrete wavelet transform. Pattern Recogn 39(6):1126–1139CrossRefzbMATHGoogle Scholar
  12. 12.
    Chuang K-S, Tzeng H-L, Chen S, Wu J, Chenc T-J (2006) Fuzzy c-means clustering with spatial information for image segmentation. Comput Med Imaging Graph 30(1):9–15CrossRefGoogle Scholar
  13. 13.
    Guler S, Liang WH, Pushee IA (2003) A Video Event Detection and Mining Framework. Conference on computer vision and pattern recognition workshop, USA 4Google Scholar
  14. 14.
    Islam S, Ahmed DM (2013) Implementation of image segmentation for natural images using clustering methods. International Journal of Emerging Technology and Advanced Engineering 3(3)Google Scholar
  15. 15.
    Iÿnkaya T, Kayaligil S, ozdemirel NE (2015) Ant colony optimization based clustering methodology. Appl Soft Comput 28:301–311CrossRefGoogle Scholar
  16. 16.
    Jun B, Kim D (2012) Robust face detection using local gradient patterns and evidence accumulation. Pattern Recogn 45(9):3304–3316CrossRefGoogle Scholar
  17. 17.
    Karnan M, Logheshwari T (2010) Improved implementation of brain MRI image segmentation using ant colony system. IEEE international conference on computational intelligence and computing research (ICCIC), pp 1–4Google Scholar
  18. 18.
    Laumer M, Amon P, Hutter A, Kaup A (2016) Moving object detection in the H. 264/AVC compressed domain. APSIPA Transactions on Signal and Information Processing 5Google Scholar
  19. 19.
    Li L-J, Socher R, Fei-Fei L (2009) Towards total scene understanding: Classification, annotation and segmentation in an automatic framework. IEEE conference on computer vision and pattern recognition, pp 2036–2043Google Scholar
  20. 20.
    Liu C, Yuen PC, Qiu G (2009) Object motion detection using information theoretic spatio-temporal saliency. Pattern Recogn 42(11):2897–2906CrossRefzbMATHGoogle Scholar
  21. 21.
    Lu S, Zhang J, Feng D (2007) An efficient method for detecting ghost and left objects in surveillance video. IEEE conference on advanced video and signal based surveillance, London, UK, pp 540–545Google Scholar
  22. 22.
    Mahalingam T, Subramoniam M (2018) A robust single and multiple moving object detection, tracking and classification. Applied Computing and InformaticsGoogle Scholar
  23. 23.
    Mahesh C, Pawaskar NSN, Saurabh SA (2014) Detection of moving object based on background subtraction. International Journal of emerging Trends & Technology in Computer Science 3(3):215–218Google Scholar
  24. 24.
    Meena A, Raja K (2013) Spatial fuzzy C-means PET image segmentation of neurodegenerative disorder. Indian Journal of Computer Science and Engineering 4(1)Google Scholar
  25. 25.
    Negri P, Goussies N, Lotito P (2014) Detecting pedestrians on a movement feature space. Pattern Recogn 47(1):56–71CrossRefGoogle Scholar
  26. 26.
    Panda DK, Meher S (2016) Detection of moving objects using fuzzy color difference histogram based background subtraction. IEEE Signal Processing Letters 23(1):45–49CrossRefGoogle Scholar
  27. 27.
    Pintea C-M, Ticala C (2016) Medical image processing: A brief survey and a new theoretical hybrid ACO model. Springer smart innovation systems and technologies, pp 117–134Google Scholar
  28. 28.
    Pugazhenthi A, Sreenivasulu G, Indhirani A (2015) Background removal by modified fuzzy c-means clustering algorithm. IEEE international conference on engineering and technology (ICETECH), pp 1–3Google Scholar
  29. 29.
    Ray KS, Chakraborty S (2018) Object detection by spatio-temporal analysis and tracking of the detected objects in a video with variable background. J Vis Commun Image Represent 58:662–674CrossRefGoogle Scholar
  30. 30.
    Ren Y, Li X, Hu H, Xi J (2009) Detection and tracking of multiple targets based on video processing. Second international conference on intelligent computation technology and automation (ICICTA), pp 586–589Google Scholar
  31. 31.
    Sabirin H, Kim M (2012) Moving object detection and tracking using a spatio-temporal graph in H. 264/AVC bit streams for video surveillance. IEEE Transactions on Multimedia 14(3):657–668CrossRefGoogle Scholar
  32. 32.
    Sharmila Sujatha G, Valli Kumari V (2015) An efficient motion based video object detection and tracking system. International conference on innovative trends in electronics communications and applications (ICIECA15), vol 1, issue 13. pp 85–97Google Scholar
  33. 33.
    Sheikh A, Krishna RK (2011) Segmentation of brain MRI for tumor detection using ant colony optimization. Proc. of Int. Colloquiums on Computer Electronics, Electrical, Mechanical and CivilGoogle Scholar
  34. 34.
    Shuai H, Liu Q, Zhang K, Yang J, Deng J (2017) Cascaded regional Spatio-temporal feature-routing networks for video object detection. IEEE Access 6Google Scholar
  35. 35.
    Spagnolo P, Orazio TD, Leo M, Distante A (2006) Moving object segmentation by background subtraction and temporal analysis. J Image Vision Comput 24(5):411–423CrossRefGoogle Scholar
  36. 36.
    Tian S, Yuan F, Xia G-S (2016) Multi-object tracking with inter-feedback between detection and tracking. Neurocomputing 171:768–780CrossRefGoogle Scholar
  37. 37.
    Xiao F, Peng L, Fu L, Gao X (2018) Salient object detection based on eye tracking data. Signal Process 144:392–397CrossRefGoogle Scholar
  38. 38.
    Xiong G, Zhou X, Ji L (2006) Automated segmentation of drosophila RNAi fluorescence cellular images using deformable models. IEEE Transactions on Circuits and Systems 53(11)Google Scholar
  39. 39.
    Xu S, Hu L, Yang X, Liu X (2013) A cluster number adaptive fuzzy c-means algorithm for image segmentation. International Journal of Signal Processing, Image Processing and Pattern Recognition 6(5):191–204CrossRefGoogle Scholar
  40. 40.
    Xu X, Liang T, Wang G, Wang M, Wang X (2016) Self-adaptive PCNN based on the ACO algorithm and its application on medical image segmentation. Journal of Intelligent Automation & Soft Computing 23(2):303–310CrossRefGoogle Scholar
  41. 41.
    Ye Y, Ci S, Liu Y, Tang H (2011) Dynamic video object detection with single PTU camera. IEEE conference on visual communications and image processing, pp 1–4Google Scholar
  42. 42.
    Zhang S, Wang C, Chan S-C, Wei X, Ho C-H (2015) New object detection, tracking, and recognition approaches for video surveillance over camera network. IEEE Sensors J 15(5):2679–2691CrossRefGoogle Scholar

Copyright information

© Springer Science+Business Media, LLC, part of Springer Nature 2019

Authors and Affiliations

  1. 1.Sathyabama UniversityChennaiIndia

Personalised recommendations