Crowd analysis using Bayesian Risk Kernel Density Estimation

Engineering Applications of Artificial Intelligence - Tập 82 - Trang 282-293 - 2019
Mahnaz Razavi1, Hadi Sadoghi Yazdi1, Amir Hossein Taherinia1
1Department of Computer Engineering, Faculty of Engineering, Ferdowsi University Of Mashhad (FUM), Mashhad, Iran

Tài liệu tham khảo

Ankerst, 1999, OPTICS: ordering points to identify the clustering structure, 49 Boyd, 2004 Breunig, 1999, Optics-of: Identifying local outliers, 262 Brown, 2007, Automatic panoramic image stitching using invariant features, Int. J. Comput. Vis., 74, 59, 10.1007/s11263-006-0002-3 Chen, Ke, Gong, Shaogang, Xiang, Tao, Change Loy, Chen, 2013. Cumulative attribute space for age and crowd density estimation. In: Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, pp. 2467–2474. Chen, 2012, Feature mining for localised crowd counting, 3 Cheng, 1995, Mean shift, mode seeking, and clustering, IEEE Trans. Pattern Anal. Mach. Intell., 17, 790, 10.1109/34.400568 Comaniciu, 2002, Mean shift: A robust approach toward feature space analysis, IEEE Trans. Pattern Anal. Mach. Intell., 24, 603, 10.1109/34.1000236 Current World Population, 2018 Dalal, 2005, Histograms of oriented gradients for human detection, 886 Ester, 1996, A density-based algorithm for discovering clusters in large spatial databases with noise, 226 Ferryman, 2009, Pets2009: Dataset and challenge, 1 Fradi, 2013, Crowd density map estimation based on feature tracks, 040 Fradi, 2015, Spatio-temporal crowd density model in a human detection and tracking framework, Signal Process., Image Commun., 31, 100, 10.1016/j.image.2014.11.006 Fränti, 2018 Fränti, 2006, Iterative shrinking method for clustering problems, Pattern Recognit., 39, 761, 10.1016/j.patcog.2005.09.012 Fu, 2015, Fast crowd density estimation with convolutional neural networks, Eng. Appl. Artif. Intell., 43, 81, 10.1016/j.engappai.2015.04.006 Geng, 2018, RECOME: a new density-based clustering algorithm using relative KNN kernel density, Inform. Sci., 436, 13, 10.1016/j.ins.2018.01.013 Han, 2011 He, 2017, A kernel-power-density-based algorithm for channel multipath components clustering, IEEE Trans. Wirel. Commun., 16, 7138, 10.1109/TWC.2017.2740206 Howard, 2017 Hunter, 2012 Idrees, 2015, Detecting humans in dense crowds using locally-consistent scale prior and global occlusion reasoning, IEEE Trans. Pattern Anal. Mach. Intell., 37, 1986, 10.1109/TPAMI.2015.2396051 Kaufman, 2009 Kriegel, 2011, Density-based clustering, Wiley Interdiscip. Rev. Data Mining Knowl. Discov., 1, 231, 10.1002/widm.30 Liu, 2016, Ssd: Single shot multibox detector, 21 MacQueen, 1967, Some methods for classification and analysis of multivariate observations, 281 Network live IP video cameras directory Insecam.com, 2017 Park, 2009, A simple and fast algorithm for K-medoids clustering, Expert Syst. Appl., 36, 3336, 10.1016/j.eswa.2008.01.039 Parzen, 1962, On estimation of a probability density function and mode, Annal. Math. Stat., 33, 1065, 10.1214/aoms/1177704472 Qiu, 2017, User clustering in a dynamic social network topic model for short text streams, Inform. Sci., 414, 102, 10.1016/j.ins.2017.05.018 Rasmussen, 2000, The infinite Gaussian mixture model, 554 Redmon, 2018 Rodriguez, 2011, Density-aware person detection and tracking in crowds, 2423 Rokach, 2005, Clustering methods, 321 Saleh, 2015, Recent survey on crowd density estimation and counting for visual surveillance, Eng. Appl. Artif. Intell., 41, 103, 10.1016/j.engappai.2015.01.007 Schneider, 2013, Fast parameterless density-based clustering via random projections, 861 Sibson, 1973, SLINK: an optimally efficient algorithm for the single-link cluster method, Comput. J., 16, 30, 10.1093/comjnl/16.1.30 Silverman, 1986 Sindagi, 2018, A survey of recent advances in cnn-based single image crowd counting and density estimation, Pattern Recognit. Lett., 107, 3, 10.1016/j.patrec.2017.07.007 Steinwart, 2008 Xu, 1998, A distribution-based clustering algorithm for mining in large spatial databases, 324 Xu, 2015, A comprehensive survey of clustering algorithms, Ann. Data Sci., 2, 165, 10.1007/s40745-015-0040-1 Ye, 2003, Color image segmentation using density-based clustering, II