• Corpus ID: 15682504

Crowd Pedestrian Counting Considering Network Flow Constraints in Videos

@article{Gao2016CrowdPC,
  title={Crowd Pedestrian Counting Considering Network Flow Constraints in Videos},
  author={Liqing Gao and Yanzhang Wang and Xin Ye and Jian Wang},
  journal={ArXiv},
  year={2016},
  volume={abs/1605.03821}
}
A quadratic programming method with network flow constraints is proposed to improve crowd pedestrian counting in video surveillance. Most of the existing approaches estimate the number of pedestrians within one frame, which result in inconsistent predictions in temporal domain. In this paper, firstly, we segment the foreground of each frame into different groups, each of which contains several pedestrians. Then we train a regression-based map from low level features of each group to its person… 

Figures and Tables from this paper

People counting with block histogram features and network flow constraints
  • Liqing Gao, Yanzhang Wang, Jian Wang
  • Computer Science
    2016 9th International Congress on Image and Signal Processing, BioMedical Engineering and Informatics (CISP-BMEI)
  • 2016
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This paper designs block histogram features for each group of people and proposes a quadratic programming method with network flow constraints on contracted graphs to refine the crowd counting results.
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An intelligent CCTV crowd counting system based on two algorithms that estimate the density of each pixel in each frame and use it as a basis for counting people that is more practical than the state of the art regression methods and easy to deploy.

References

SHOWING 1-10 OF 36 REFERENCES
Privacy preserving crowd monitoring: Counting people without people models or tracking
We present a privacy-preserving system for estimating the size of inhomogeneous crowds, composed of pedestrians that travel in different directions, without using explicit object segmentation or
Real-time people counting from depth imagery of crowded environments
TLDR
A system for automatic people counting in crowded environments that performs foreground/background segmentation on depth image streams in order to coarsely segment persons, then depth information is used to localize head candidates which are then tracked in time on an automatically estimated ground plane.
Multi-Commodity Network Flow for Tracking Multiple People
In this paper, we show that tracking multiple people whose paths may intersect can be formulated as a multi-commodity network flow problem. Our proposed framework is designed to exploit image
Counting People With Low-Level Features and Bayesian Regression
TLDR
An approach to the problem of estimating the size of inhomogeneous crowds, which are composed of pedestrians that travel in different directions, without using explicit object segmentation or tracking is proposed, using the mixture of dynamic-texture motion model and Bayesian regression.
Detection and Tracking of Occluded People
TLDR
This work observes that typical occlusions are due to overlaps between people and proposes a people detector tailored to various occlusion levels, and leverages the fact that person/person Occlusion result in very characteristic appearance patterns that can help to improve detection results.
Counting moving persons in crowded scenes
TLDR
The most important peculiarity of the proposed method is the availability of a simple training procedure using a brief video sequence that shows a person walking around in the scene that automatically evaluates all the parameters needed by the system, thus making the method particularly suited for end-user applications.
From Semi-supervised to Transfer Counting of Crowds
TLDR
This study proposes a unified active and semi-supervised regression framework with ability to perform transfer learning, by exploiting the underlying geometric structure of crowd patterns via manifold analysis.
Pedestrian counting based on spatial and temporal analysis
TLDR
By proposing the novel spatial-temporal matrix, a more robust and efficient pedestrian counting algorithm can be developed and achieves satisfying performances in terms of both accuracy and efficiency.
Robust people counting using sparse representation and random projection
Joint tracking and segmentation of multiple targets
TLDR
This work proposes a multi-target tracker that exploits low level image information and associates every (super)-pixel to a specific target or classifies it as background and obtains a video segmentation in addition to the classical bounding-box representation in unconstrained, real-world videos.
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