caa a22 4500 605541469 CHVBK 20210128100916.0 cr unu---uuuuu 210128e20151101xx s 000 0 eng 10.1007/s00371-014-1032-4 doi (NATIONALLICENCE)springer-10.1007/s00371-014-1032-4 Estimation of crowd density by clustering motion cues [Elektronische Daten] [Aravinda Rao, Jayavardhana Gubbi, Slaven Marusic, Marimuthu Palaniswami] Understanding crowd behavior using automated video analytics is a relevant research problem in recent times due to complex challenges in monitoring large gatherings. From an automated video surveillance perspective, estimation of crowd density in particular regions of the video scene is an indispensable tool in understanding crowd behavior. Crowd density estimation provides the measure of number of people in a given region at a specified time. While most of the existing computer vision methods use supervised training to arrive at density estimates, we propose an approach to estimate crowd density using motion cues and hierarchical clustering. The proposed method incorporates optical flow for motion estimation, contour analysis for crowd silhouette detection, and clustering to derive the crowd density. The proposed approach has been tested on a dataset collected at the Melbourne Cricket Ground (MCG) and two publicly available crowd datasets—Performance Evaluation of Tracking and Surveillance (PETS) 2009 and University of California, San Diego (UCSD) Pedestrian Traffic Database—with different crowd densities (medium- to high-density crowds) and in varied environmental conditions (in the presence of partial occlusions). We show that the proposed approach results in accurate estimates of crowd density. While the maximum mean error of $$3.62$$ 3.62 was received for MCG and PETS datasets, it was $$2.66$$ 2.66 for UCSD dataset. The proposed approach delivered superior performance in $$50~\%$$ 50 % of the cases on PETS $$2009$$ 2009 dataset when compared with existing methods. Springer-Verlag Berlin Heidelberg, 2014 Video surveillance nationallicence Crowd nationallicence Density estimation nationallicence People counting nationallicence Crowd monitoring nationallicence Optical flow nationallicence Clustering nationallicence Rao Aravinda ISSNIP, Department of Electrical and Electronic Engineering, The University of Melbourne, 3010, Parkville, VIC, Australia aut Gubbi Jayavardhana ISSNIP, Department of Electrical and Electronic Engineering, The University of Melbourne, 3010, Parkville, VIC, Australia aut Marusic Slaven ISSNIP, Department of Electrical and Electronic Engineering, The University of Melbourne, 3010, Parkville, VIC, Australia aut Palaniswami Marimuthu ISSNIP, Department of Electrical and Electronic Engineering, The University of Melbourne, 3010, Parkville, VIC, Australia aut The Visual Computer Springer Berlin Heidelberg 31/11(2015-11-01), 1533-1552 0178-2789 31:11<1533 2015 31 371 https://doi.org/10.1007/s00371-014-1032-4 text/html Onlinezugriff via DOI BK010053 XK010053 XK010000 Metadata rights reserved Springer special CC-BY-NC licence nationallicence 1 research-article jats NATIONALLICENCE NATIONALLICENCE NL-springer NATIONALLICENCE 856 40 https://doi.org/10.1007/s00371-014-1032-4 text/html Onlinezugriff via DOI NATIONALLICENCE 700 1- Rao Aravinda ISSNIP, Department of Electrical and Electronic Engineering, The University of Melbourne, 3010, Parkville, VIC, Australia aut NATIONALLICENCE 700 1- Gubbi Jayavardhana ISSNIP, Department of Electrical and Electronic Engineering, The University of Melbourne, 3010, Parkville, VIC, Australia aut NATIONALLICENCE 700 1- Marusic Slaven ISSNIP, Department of Electrical and Electronic Engineering, The University of Melbourne, 3010, Parkville, VIC, Australia aut NATIONALLICENCE 700 1- Palaniswami Marimuthu ISSNIP, Department of Electrical and Electronic Engineering, The University of Melbourne, 3010, Parkville, VIC, Australia aut NATIONALLICENCE 773 0- The Visual Computer Springer Berlin Heidelberg 31/11(2015-11-01), 1533-1552 0178-2789 31:11<1533 2015 31 371