Abstract
Moving object detection is a widely used and important research topic in computer vision and video processing. Foreground aperture, ghosting and sudden illumination changes are the main problems in moving object detection. To consider the above problems, this work proposes two approaches: (i) improved three-frame difference method and (ii) combining background subtraction and improved three-frame difference method for the detection of multiple moving objects from indoor and outdoor real video dataset. This work accurately detects the moving objects with varying object size and number in different complex environments. We compute the detection error and processing time of two proposed as well as previously existing approaches. Experimental results and error rate analysis show that our methods detect the moving targets efficiently and effectively as compared to the traditional approaches.
Article PDF
Similar content being viewed by others
Avoid common mistakes on your manuscript.
References
Zhang, Y.; Liu, X.; Chang, M.C.; Ge, W.; Chen, T.: Spatio-temporal phrases for activity recognition. In: Computer Vision-ECCV, vol. 7574, pp. 707–721. Springer, Berlin, Heidelberg (2012)
Sengar, S.S.; Mukhopadhyay, S.: Moving object tracking using Laplacian-dct based perceptual hash. In: International Conference on Wireless Communications, pp. 2345–2349. Signal Processing and Networking, IEEE (2016b)
Fu, K.S.; Gonzalez, R.; Lee, C.G.: Robotics: Control, Sensing, vision, and Intelligence. Tata McGraw-Hill Edition, Delhi (2008)
Aslandogan, Y.A.; Yu, C.T.: Techniques and systems for image and video retrieval. IEEE Trans. Knowl. Data Eng. 11(1), 56–63 (1999)
Sengar, S.S.; Mukhopadhyay, S.: Moving object area detection using normalized self adaptive optical flow. Optik-Int. J. Light Electron Optics 127(16), 6258–6267 (2016a)
Sengar, S.S.; Mukhopadhyay, S.: A novel method for moving object detection based on block based frame differencing. In: 3rd International Conference on Recent Advances in Information Technology, IEEE, pp. 462–472 (2016c)
Sobral, A.; Vacavant, A.: A comprehensive review of background subtraction algorithms evaluated with synthetic and real videos. Comput. Vis. Image Underst. 122, 4–21 (2014)
Kameda, Y.; Minoh, M.: A human motion estimation method using three-successive video frames. In: ICVSM, pp. 135–140 (1996)
Fei, M.; Li, J.; Liu, H.: Visual tracking based on improved foreground detection and perceptual hashing. Neurocomputing 152, 413–428 (2015)
Benezeth, Y.; Jodoin, P.; Emile, B.; Laurent, H; Rosenberger, C.: Review and evaluation of commonly-implemented background subtraction algorithms. In: Pattern Recognition, IEEE Int’l Conference, pp 1–4 (2008)
Cristani, M.; Farenzena, M.; Bloisi, D.; Murino, V.: Background subtraction for automated multisensor surveillance: a comprehensive review. EURASIP J. Adv. Signal Process. 2010, 1–24 (2010)
Zhang, X.; Yang, J.: Foreground segmentation based on selective foreground model. Electron. Lett. IEEE 44(14), 851–852 (2008)
Ko, T.; Soatto, S.; Estrin, D.: Background subtraction on distributions. In: Computer Vision-ECCV, Lecture Notes in Computer Science 5304, pp. 276–289 (2008)
Li, L.; Huang, W.; Gu, I.; Tian, Q.: Foreground object detection from videos containing complex background. In: Multimedia 11th ACM International Conference, Berkeley, USA, pp. 2–10 (2003)
Kolmogorov, V.; Criminisi, A.; Blake, A.; Cross, G.; Rother, C.: Probabilistic fusion of stereo with color and contrast for bi-layer segmentation. IEEE Trans. Pattern Anal. Mach. Intell. 28(9), 1480–1492 (2006)
Barnich, O.; Droogenbroeck, MV.: Vibe: a powerful random technique to estimate the background in video sequences. In: Acoustics, Speech, and Signal Processing, International Conference, pp. 945–948 (2009)
Barnich, O.; Droogenbroeck, M.V.: Vibe: a universal background subtraction algorithm for video sequences. IEEE Trans. Image Process. 20(6), 1709–1724 (2011)
Li, L.; Huang, W.M.; Gu, I.H.; Tian, Q.: Statistical modeling of complex background for foreground object detection. IEEE Trans. Image Process. 13(11), 1459–1472 (2004)
Hosaka, T.; Kobayashi, T.; Otsu, N.: Object detection using background subtraction and foreground motion estimation. IPSJ Trans. Comput. Vision Appl. 3, 9–20 (2011)
Wolf, C.; Jolion, JM.: Integrating a discrete motion model into gmm based background subtraction. In: Pattern Recognition, 20th IEEE International Conference, pp. 9–12 (2010)
Zhang, W.; Fang, X.Z.; Yang, X.: Moving vehicles segmentation based on bayesian framework for gaussian motion model. Pattern Recognit Lett. 27(9), 956–967 (2006)
Hu, W.C.; Chen, C.H.; Chen, C.M.; Chen, T.Y.: Effective moving object detection from videos captured by a moving camera. In: Intelligent Data Analysis and Applications, Euro-China Conference 1, pp. 343–353 (2014)
Ghosh, A.; Subudhi, B.N.; Ghosh, S.: Object detection from videos captured by moving camera by fuzzy edge incorporated markov random field and local histogram matching. IEEE Trans. Circuits Syst. Video Technol. 22(8), 1127–1135 (2012)
Wang, Y.: Joint random field model for all-weather moving vehicle detection. IEEE Trans. Image Process. 19(9), 2491–2501 (2010)
Yang, J.; Yang, W.; Li, M.: An efficient moving object detection algorithm based on improved GMM and cropped frame technique. In: Mechatronics and Automation, IEEE International Conference on, pp. 658–663 (2012)
Author information
Authors and Affiliations
Corresponding author
Rights and permissions
About this article
Cite this article
Sengar, S.S., Mukhopadhyay, S. Foreground Detection via Background Subtraction and Improved Three-Frame Differencing. Arab J Sci Eng 42, 3621–3633 (2017). https://doi.org/10.1007/s13369-017-2672-2
Received:
Accepted:
Published:
Issue Date:
DOI: https://doi.org/10.1007/s13369-017-2672-2