Abstract
The identification of human actors in various scenarios with dynamic backgrounds has increased significance in numerous video-based applications. Lot of problems relevant to the detection of actors in scenarios with a dynamic background has to be addressed. This work proposes an approach to identify human actions with dynamic backgrounds and illumination variations. To accurately detect the foreground, the proposed method applies background modeling. The proposed method was assessed with approaches such as FrmDiff, MoG and ACMMM03. The assessment was performed on renowned datasets such as CDnet,WALLFLOWER and I2R. The results of assessment demonstrate the accuracy and the enhancement of the proposed method over the other methods taken into account.
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References
Barnich O, Van Droogenbroeck M (2011) ViBe: a universal background subtraction algorithm for video sequences. IEEE Trans Image Process 20(6):1709–1724
Chakraborty B, Holte MB, Moeslund TB, Gonzàlez J (2012) Selective spatio-temporal interest points. Comput Vis Image Underst 116(3):396–410
Chen T, Lu S (2017) Object-level motion detection from moving cameras. IEEE Trans Circuits Syst Video Technol 27(11):2333–2343
de Almeida IR, Cassol VJ, Badler NI, Musse SR, Jung CR (2017) Detection of global and local motion changes in human crowds. IEEE Trans. Circuits Syst. Video Technol. 27(3):603–612
Gaba N, Barak N, Aggarwal S (2016) Motion detection, tracking and classification for automated Video Surveillance, in 2016 IEEE 1st International Conference on Power Electronics, Intelligent Control and Energy Systems (ICPEICES), , pp. 1–5
Kaewtrakulpong P, Bowden R (2001) An Improved Adaptive Background Mixture Model for Real- time Tracking with Shadow Detection, Adv. Video Based Surveill. Syst., pp. 1–5
Kim K, Chalidabhongse TH, Harwood D, Davis L (2005) Real-time foreground- background segmentation using codebook model. Real-Time Imaging 11(3):172–185
Kim SW, Yun K, Yi KM, Kim SJ, Choi JY (2013) Detection of moving objects with a moving camera using non-panoramic background model. Mach Vis Appl 24(5):1015–1028
Li L, Huang W, Gu IYH, Tian Q (2003) Foreground object detection from videos containing complex background, in Proceedings of the eleventh ACM international conference on Multimedia, pp. 2–10
Liao S, Zhao G, Kellokumpu V, Pietikäinen M, Li SZ (2010) Modeling pixel process with scale invariant local patterns for background subtraction in complex scenes, in Proceedings of the IEEE Computer Society Conference on Computer Vision and Pattern Recognition, pp. 1301–1306
Lim T, Han B, Han JH (2012) Modeling and segmentation of floating foreground and background in videos. Pattern Recogn 45(4):1696–1706
Lipton AJ, Fujiyoshi H, Patil RS (1998) Moving target classification and tracking from real-time video, in 4th IEEE Workshop on Applications of Computer Vision, vol. 98, no. 2, pp. 8–14
Liu Z, Shen H, Feng G, Hu D (2012) Tracking objects using shape context matching. Neurocomputing 83:47–55
Narayana M, Hanson A, Learned-Miller EG (2014) Background subtraction: separating the modeling and the inference. Mach Vis Appl 25(5):1163–1174
Shah M, Deng JD, Woodford BJ (2014) Video background modeling: recent approaches, issues and our proposed techniques. Mach Vis Appl 25(5):1105–1119
Shen P (2013) Flow detection based on traffic video image processing. J Multimed 8(5):519–526
Tran KN, Kakadiaris IA, Shah SK (2012) Part-based motion descriptor image for human action recognition. Pattern Recogn 45(7):2562–2572
Wang Y, Lu Q, Wang D, Liu W (2015) Compressive background modeling for foreground extraction. J Electr Comput Eng 2015:1–8
Wu H, Liu N, Luo X, Su J, Chen L (2014) Real-time background subtraction-based video surveillance of people by integrating local texture patterns. Signal, Image Video Process 8(4):665–676
Yin J, Tian G, Zhou F, Jiang H (2011) Human action recognition in daily living. J Comput Inf Syst 10:3599–3605
Zhong B et al (2014) Robust tracking via patch-based appearance model and local background estimation. Neurocomputing 123:344–353
Zhou Z, Jin Z (2016) Two-dimension principal component analysis-based motion detection framework with subspace update of background. IET Comput Vis 10(6):603–612
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K., A., V., A. & N.R., R. Identification of human actor in various scenarios by applying background modeling. Multimed Tools Appl 79, 3879–3891 (2020). https://doi.org/10.1007/s11042-019-7443-5
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DOI: https://doi.org/10.1007/s11042-019-7443-5