Binghua Guo, Hongyue Dai, and Zhonghua Li
 L. Maddalena and A. Petrosino, A self-organizing approachto background subtraction for visual surveillance applica-tions, IEEE Transactions on Image Processing, 17(7), 2008,1168–1177.
 R.J. Radke, S. Andra, O. Al-Kofahi, and B. Roysam, Im-age change detection algorithms: A systematic survey, IEEETransactions on Image Processing, 14(3), 2005, 294–307.
 S. Mota, E. Ros, E.M. Ortigosa, and F.J. Pelayo, Bio-inspiredmotion detection for a blind spot overtaking monitor, Inter-national Journal of Robotics and Automation, 19(4), 2004,190–196.
 Q. Baig, M. Perrollaz, and C. Laugier, A robust motion detec-tion technique for dynamic environment monitoring: A frame-work for grid-based monitoring of the dynamic environment,IEEE Robotics & Automation Magazine, 21(1), 2014, 40–48.
 M. Kamaraj, Balakrishnan, An improved motion detectionand tracking of active blob for video surveillance, Proc. 4thICCCNT, Tiruchengode, India, 2013, 1–7.
 T. Moeslund, A. Hilton, and V. Kruger, A survey of advancesin vision-based human motion capture and analysis, ComputerVision and Image Understanding, 104(2–3), 2006, 90–126.
 O. Marques, L.M. Mayron, G.B. Borba, and H.R. Gamba,An attention-driven model for grouping similar images withimage retrieval applications, EURASIP Journal of Advancesin Signal Processing, 2007(1) 2007, 1–17.
 D. ´Culibrk, M. Mirkovi´c, V. Zlokolica, M. Pokri´c, V. Crnojevi´c,and D. Kukolj, Salient motion features for video qualityassessment, IEEE Transactions on Image Processing, 20(4),2011 948–958.
 Y. Yu, J. Gu, G.K. Mann, R.G. Gosine, Development andevaluation of object-based visual attention for automatic per-ception of robots, IEEE Transactions on Automation Scienceand Engineering, 10(2), 2013, 365–379.
 L. Itti, C. Koch, and E. Niebur, A model of saliency-basedvisual attention for rapid scene analysis, IEEE Transactionson Pattern Analysis and Machine Intelligence, 20(11), 1998,1254–1259.
 Y. Wakuda, K. Sekiyama, and T. Fukuda, Dynamic eventinterpretation and description from visual scene based oncognitive ontology for recognition by a robot, InternationalJournal of Robotics & Automation, 24(3), 2009, 263–279.
 A. Kimura, R. Yonetani, T. Hirayama, Computational modelsof human visual attention and their implementations: A survey,IEICE Transactions on Information and Systems, E96–D(3),2013, 562–578.
 S. Frintrop, E. Rome, H.I. Christensen, Computational visualattention systems and their cognitive foundations: A survey,ACM: Transaction on Applied Perception, 7(1), 2010, 1–39.
 C. Koch, S. Ullman, Shifts in selective visual attention: towardsthe underlying neural circuitry, Human Neurobiology, 4(4),1985, 219–227.
 L. Itti and P. Baldi, Bayesian surprise attracts human attention,Vision Research, 49(10), 2009, 1295–306.
 P. Baldi and L. Itti, Of bits and wows: A Bayesian theoryof surprise with applications to attention, Neural Networks,23(5), 2010, 649–666.
 M.T. L´opez, M.A. Fern´andez, A. Fern´andez-Caballero, J. Mira,and A. Delgado, Dynamic visual attention model in imagesequences, Image and Vision Computing, 25(5), 2007, 597–613.
 M. Mancas, N. Riche, J. Leroy, and B. Gosselin, Abnormalmotion selection in crowds using bottom-up saliency, Proc.18th IEEE Int. Conf. on Image Processing, Brussels, Belgium,2011, 229–232.
 D. Culibrk, S. Sladojevic, N. Riche, M. Mancas, and V.Crnojevic, Data-driven approach to dynamic visual attentionmodelling, Proc. SPIE, Brussels, Belgium, 2012, 84360N1-11.
 A. Bur. Computer models of dynamic visual attention, DoctoralDissertation, Universit´e de Neuchatel, Switzerland, 2009.
 A. Bur, P. Wurtz, R.M. M¨uri, and H. H¨ugli, Dynamic visualattention: Motion direction versus motion magnitude, Proc.IS&T/SPIE 20th Annual Symposium on Electronic Imaging,San Jose, USA, 2008, 1–12.
 R. Szeliki, Computer vision: algorithms and applications, 1sted. (USA: Springer, 2010).
 M. Zuliani, C.S. Kenney, and B.S. Manjunath, The multi-RANSAC algorithm and its application to detect planar ho-mographies, Proc. of IEEE Computer Society Conference onComputer Vision and Pattern Recognition. San Diego, USA,2005, 153–156.
 D. Comaniciu and P. Meer, Mean shift: a robust approachtoward feature space analysis, IEEE Transactions on PatternAnalysis and Machine Intelligence, 24(5), 2002, 603–619.
 A. Murarka. Building safety maps using vision for safe localmobile robot navigation, Doctoral Dissertation, The Universityof Texas at Austin, Austin, 2009.
 D.G. Lowe. Distinctive image features from scale-invariantkeypoints, International Journal of Computer Vision, 60(2),2004, 91–110.
 H. Bay, T. Tuytelaars, and L. Van Gool, Surf: speededup robust features, Proc. European Conf. Computer Vision,Berlin, Springer, 2006, 404–417.