Real-Time People Counting Using Multiple Lines

Barandiarán, Javier Avilés; Murguia, B.; Boto, Fernando

doi:10.1109/wiamis.2008.27

Cited by 66 publications

(26 citation statements)

References 9 publications

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“…We compared the proposed algorithm with recently published method for people counting by Barandiaran et al [11]. The result of this comparison is given in Table 1.…”

Section: Experimental Results and Analysismentioning

confidence: 99%

See 1 more Smart Citation

K-means based segmentation for real-time zenithal people counting

Antic

Letic

Ćulibrk

et al. 2009

2009 16th IEEE International Conference on Image Processing (ICIP)

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The paper presents an efficient and reliable approach to automatic people segmentation, tracking and counting, designed for a system with an overhead mounted (zenithal) camera. Upon the initial block-wise background subtraction, k-means clustering is used to enable the segmentation of single persons in the scene. The number of people in the scene is estimated as the maximal number of clusters with acceptable inter-cluster separation. Tracking of segmented people is addressed as a problem of dynamic cluster assignment between two consecutive frames and it is solved in a greedy fashion. Systems for people counting are applied to people surveillance and management and lately within the ambient intelligence solutions. Experimental results suggest that the proposed method is able to achieve very good results in terms of counting accuracy and execution speed.

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“…We compared the proposed algorithm with recently published method for people counting by Barandiaran et al [11]. The result of this comparison is given in Table 1.…”

Section: Experimental Results and Analysismentioning

confidence: 99%

“…Barandiaran et al [11] have recently described a real-time people counting system, that uses a single overhead mounted camera, and achieves counting accuracy of 95%. Counting is performed inside the image zone with multiple counting lines placed over it.…”

Section: Introductionmentioning

confidence: 99%

K-means based segmentation for real-time zenithal people counting

Antic

Letic

Ćulibrk

et al. 2009

2009 16th IEEE International Conference on Image Processing (ICIP)

View full text Add to dashboard Cite

show abstract

“…This task is quite different from crowd counting, in that it concerns itself with the number of people passing a specific point in space over time, rather than the total number of people in a space at any given instant. Several approaches have sought to use used overhead cameras (Albiol, Albiol, & Silla, 2009;Barandiaran, Murguia, & Boto, 2008;Chen, 2003;Chen, Chen, & Chen, 2006;Kim, Choi, Choi, & Ko, 2002;Terada, Yoshida, Oe, & Yamaguchi, 1999;Velipasalar, Tian, & Hampapur, 2006), from which people can be easily located and counted through motion segmentation. However, a solution such as this is not appropriate for the vast majority of existing CCTV infrastructure.…”

Section: Systemmentioning

confidence: 99%

Automatic surveillance in transportation hubs: No longer just about catching the bad guy

Denman

Kleinschmidt

Ryan

et al. 2015

Expert Systems with Applications

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“…To count people in entrance/exit gates and in elevator zones, the top-view viewpoint was usually used [8]- [10], [12]. There are almost no occluded people under this viewpoint thus it is easy to segment and count individuals.…”

Section: Related Workmentioning

confidence: 99%

“…To overcome the bottleneck, some existing methods are restricted to work in specific camera viewpoints, i.e. top-view viewpoint [8]- [12]. In surveillance, oblique settings of cameras are preferable due to a wider field of view.…”

Section: Introductionmentioning

confidence: 99%

Real-Time Counting People in Crowded Areas by Using Local Empirical Templates and Density Ratios

Hung

Hsu

Chung

et al. 2012

IEICE Trans. Inf. & Syst.

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SUMMARYIn this paper, a fast and automated method of counting pedestrians in crowded areas is proposed along with three contributions. We firstly propose Local Empirical Templates (LET), which are able to outline the foregrounds, typically made by single pedestrians in a scene. LET are extracted by clustering foregrounds of single pedestrians with similar features in silhouettes. This process is done automatically for unknown scenes. Secondly, comparing the size of group foreground made by a group of pedestrians to that of appropriate LET captured in the same image patch with the group foreground produces the density ratio. Because of the local scale normalization between sizes, the density ratio appears to have a bound closely related to the number of pedestrians who induce the group foreground. Finally, to extract the bounds of density ratios for groups of different number of pedestrians, we propose a 3D human models based simulation in which camera viewpoints and pedestrians' proximity are easily manipulated. We collect hundreds of typical occluded-people patterns with distinct degrees of human proximity and under a variety of camera viewpoints. Distributions of density ratios with respect to the number of pedestrians are built based on the computed density ratios of these patterns for extracting density ratio bounds. The simulation is performed in the offline learning phase to extract the bounds from the distributions, which are used to count pedestrians in online settings. We reveal that the bounds seem to be invariant to camera viewpoints and humans' proximity. The performance of our proposed method is evaluated with our collected videos and PETS 2009's datasets. For our collected videos with the resolution of 320x240, our method runs in real-time with good accuracy and frame rate of around 30 fps, and consumes a small amount of computing resources. For PETS 2009's datasets, our proposed method achieves competitive results with other methods tested on the same datasets [1], [2]. key words: local empirical templates, local density ratios, density ratio bounds, and people counting

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Real-Time People Counting Using Multiple Lines

Cited by 66 publications

References 9 publications

K-means based segmentation for real-time zenithal people counting

K-means based segmentation for real-time zenithal people counting

Automatic surveillance in transportation hubs: No longer just about catching the bad guy

Real-Time Counting People in Crowded Areas by Using Local Empirical Templates and Density Ratios

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