Unique people count from monocular videos
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| 003 | CHVBK | ||
| 005 | 20210128100912.0 | ||
| 007 | cr unu---uuuuu | ||
| 008 | 210128e20151001xx s 000 0 eng | ||
| 024 | 7 | 0 | |a 10.1007/s00371-014-1022-6 |2 doi |
| 035 | |a (NATIONALLICENCE)springer-10.1007/s00371-014-1022-6 | ||
| 245 | 0 | 0 | |a Unique people count from monocular videos |h [Elektronische Daten] |c [Satarupa Mukherjee, Stephani Gil, Nilanjan Ray] |
| 520 | 3 | |a Counting unique number of people in a video (i.e., counting a person only once while the person passes through the field of view) is required in many video analytic applications, such as transit passenger and pedestrian volume count in railway stations, malls, and road intersections. The principal roadblock here is occlusion. To avoid this bottleneck, we adopt a combination of (a) a radical new approach of unique influx and outflux count (UIOC) of people within a region of interest (ROI), which is adopted from computational fluidics, (b) a nonlinear regressor to estimate the number of people within a ROI, and (c) ROI boundary tracking (as opposed to object or feature tracking) for a short period. In UIOC, we compute influx/outflux rate, i.e., number of people entering or exiting the ROI per unit time. Then, we sum the influx/outflux rate between any two time points to estimate the number of people that entered and/or left the ROI within that time interval. Our framework is validated on 19 publicly available datasets, with abundant occlusion, obtaining more than 95% accuracy for each video. Our framework is online and real time. Our framework is comparatively inexpensive to install and operate as only one camera is used. These features make the proposed framework suitable for low-cost, small-business/residential and/or commercial applications. We also extend our framework beyond monocular videos and apply it on multiple views of a publicly available dataset with about 99% accuracy. | |
| 540 | |a Springer-Verlag Berlin Heidelberg, 2014 | ||
| 690 | 7 | |a Unique people count |2 nationallicence | |
| 690 | 7 | |a Influx |2 nationallicence | |
| 690 | 7 | |a Outflux |2 nationallicence | |
| 690 | 7 | |a Occlusion |2 nationallicence | |
| 690 | 7 | |a Boundary tracking |2 nationallicence | |
| 700 | 1 | |a Mukherjee |D Satarupa |u Department of Computing Sciences, University of Alberta, Edmonton, Canada |4 aut | |
| 700 | 1 | |a Gil |D Stephani |u Department of Computing Sciences, University of Alberta, Edmonton, Canada |4 aut | |
| 700 | 1 | |a Ray |D Nilanjan |u Department of Computing Sciences, University of Alberta, Edmonton, Canada |4 aut | |
| 773 | 0 | |t The Visual Computer |d Springer Berlin Heidelberg |g 31/10(2015-10-01), 1405-1417 |x 0178-2789 |q 31:10<1405 |1 2015 |2 31 |o 371 | |
| 856 | 4 | 0 | |u https://doi.org/10.1007/s00371-014-1022-6 |q text/html |z Onlinezugriff via DOI |
| 898 | |a BK010053 |b XK010053 |c XK010000 | ||
| 900 | 7 | |a Metadata rights reserved |b Springer special CC-BY-NC licence |2 nationallicence | |
| 908 | |D 1 |a research-article |2 jats | ||
| 949 | |B NATIONALLICENCE |F NATIONALLICENCE |b NL-springer | ||
| 950 | |B NATIONALLICENCE |P 856 |E 40 |u https://doi.org/10.1007/s00371-014-1022-6 |q text/html |z Onlinezugriff via DOI | ||
| 950 | |B NATIONALLICENCE |P 700 |E 1- |a Mukherjee |D Satarupa |u Department of Computing Sciences, University of Alberta, Edmonton, Canada |4 aut | ||
| 950 | |B NATIONALLICENCE |P 700 |E 1- |a Gil |D Stephani |u Department of Computing Sciences, University of Alberta, Edmonton, Canada |4 aut | ||
| 950 | |B NATIONALLICENCE |P 700 |E 1- |a Ray |D Nilanjan |u Department of Computing Sciences, University of Alberta, Edmonton, Canada |4 aut | ||
| 950 | |B NATIONALLICENCE |P 773 |E 0- |t The Visual Computer |d Springer Berlin Heidelberg |g 31/10(2015-10-01), 1405-1417 |x 0178-2789 |q 31:10<1405 |1 2015 |2 31 |o 371 | ||