• Corpus ID: 56227347

A Literature Study on Crowd (People) Counting With the Help of Surveillance Videos

@inproceedings{Kowcika2015ALS,
  title={A Literature Study on Crowd (People) Counting With the Help of Surveillance Videos},
  author={A. Kowcika and S.Sridhar S.Sridhar},
  year={2015}
}
The categories of crowd counting in video falls in two broad categories: (a) ROI counting which estimates the total number of people in some regions at certain time instance (b) LOI counting which counts people who crosses a detecting line in certain time duration. The LOI counting can be developed using feature tracking techniques where the features are either tracked into trajectories and these trajectories are clustered into object tracks or based on extracting and counting crowd blobs from… 
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References

SHOWING 1-10 OF 54 REFERENCES
Automated people counting at a mass site
  • Ya-Li Hou, G. Pang
  • Computer Science
    2008 IEEE International Conference on Automation and Logistics
  • 2008
TLDR
This paper aims to estimate the number of people in a complicated scenario, which has around one hundred persons in an outdoors event, and several people counting methods based on crowd density are considered to find the relationship between the foreground pixels and the numberof people in the large crowd.
Robust crowd counting using detection flow
TLDR
It is argued that counting based on detection flow provides a better way to estimate the crowd size with following merits: it can greatly alleviate the common weakness of an object detector including miss detection and false alarms; it is robust to temporal object occlusions and noises; and it is more competent to give specific descriptions of the crowd, e.g. crowd moving directions and target locations.
Towards a Robust Solution to People Counting
TLDR
This paper investigates the possibilities of developing a robust statistical method for people counting and chooses not to require prior learning of categories corresponding to different number of people, and searches for a suitable way of correcting the perspective distortion.
People counting using ellipse detection and forward/backward tracing
TLDR
This paper uses forward/backward tracing to re-label the number of objects in the occluded blob by applying the ellipse detection technique, and demonstrates the effectiveness of this method.
People Counting and Human Detection in a Challenging Situation
  • Ya-Li Hou, G. Pang
  • Computer Science
    IEEE Transactions on Systems, Man, and Cybernetics - Part A: Systems and Humans
  • 2011
TLDR
An Expectation Maximization (EM)-based method has been developed to locate individuals in a low resolution scene and the number of people is used as a priori for locating individuals based on feature points.
Crossing the Line: Crowd Counting by Integer Programming with Local Features
TLDR
This work proposes an integer programming method for estimating the instantaneous count of pedestrians crossing a line of interest in a video sequence that achieves state-of-the-art performance on several challenging crowd video datasets.
An improved real-time method for counting people in crowded scenes based on a statistical approach
TLDR
Testing this method on a new dataset proved its speed and accuracy under many shooting scenarios, especially in crowded conditions where the averaging process reduces the variations in the number of detected SURF points per person.
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TLDR
A counting system which consists of four modules: foreground extraction, head-shoulder component detection, tracking and trajectory analysis, in order to reduce computation costs and cope with various complex surveillance situations for foreground extraction.
A robust method for detecting and counting people
TLDR
A robust and high accuracy of bi-directional counting can be achieved using the method described in this paper, and merge/split phenomenon is also discussed to overcome the problem of people touching together.
People Counting across Multiple Cameras for Intelligent Video Surveillance
TLDR
A multi-object detection and tracking method by means of synthesizing the local-feature-level information into object-level based on an electing and weighting mechanism (EWM) that can find the objects in overlapping FOVs and estimate the integrated number of people across multiple cameras.
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