Detecting people in dense crowds

Sim, Chern-Horng; Ekambaram, Rajmadhan; Ranganath, S.

doi:10.1007/s00138-010-0280-1

Cited by 5 publications

(3 citation statements)

References 22 publications

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“…Yao and Odobez [105] proposed to take advantage of the stationary cameras to perform background subtraction and jointly learn the appearance and the foreground shape of people in videos. Sim et al [106] proposed a representation called the colour bin image which is extracted from the initially detected windows, and they use it for training a classifier to improve the performance of the initial detector. The proposed system was applied for detecting individual heads in dense crowds of 30 to 40 people against cluttered backgrounds from a single video frame.…”

Section: Person Detection In Dense Crowds and People Countingmentioning

confidence: 99%

Human detection in surveillance videos and its applications - a review

Paul

Haque

Chakraborty

2013

EURASIP J. Adv. Signal Process.

157

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Detecting human beings accurately in a visual surveillance system is crucial for diverse application areas including abnormal event detection, human gait characterization, congestion analysis, person identification, gender classification and fall detection for elderly people. The first step of the detection process is to detect an object which is in motion. Object detection could be performed using background subtraction, optical flow and spatio-temporal filtering techniques. Once detected, a moving object could be classified as a human being using shape-based, texture-based or motion-based features. A comprehensive review with comparisons on available techniques for detecting human beings in surveillance videos is presented in this paper. The characteristics of few benchmark datasets as well as the future research directions on human detection have also been discussed. Review IntroductionOver the recent years, detecting human beings in a video scene of a surveillance system is attracting more attention due to its wide range of applications in abnormal event detection, human gait characterization, person counting in a dense crowd, person identification, gender classification, fall detection for elderly people, etc.The scenes obtained from a surveillance video are usually with low resolution. Most of the scenes captured by a static camera are with minimal change of background. Objects in the outdoor surveillance are often detected in far field. Most existing digital video surveillance systems rely on human observers for detecting specific activities in a real-time video scene. However, there are limitations in the human capability to monitor simultaneous events in surveillance displays [1]. Hence, human motion analysis in automated video surveillance has become one of the most active and attractive research topics in the area of computer vision and pattern recognition.An intelligent system detects and captures motion information of moving targets for accurate object classification. The classified object is being tracked for high-level analysis. In this study, we focus on detecting humans and do not consider recognition of their complex activities. Human detection is a difficult task from a machine vision perspective as it is influenced by a wide range of possible appearance due to changing articulated pose, clothing, lighting and background, but prior knowledge on these limitations can improve the detection performance.The detection process generally occurs in two steps: object detection and object classification. Object detection could be performed by background subtraction, optical flow and spatio-temporal filtering. Background subtraction is a popular method for object detection where it attempts to detect moving objects from the difference between the current frame and a background frame in a pixelby-pixel or block-by-block fashion. There are few available approaches to perform background subtraction. The most common ones are adaptive Gaussian mixture [2-10], non-parametric background [11][12][13][1...

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Section: Person Detection In Dense Crowds and People Countingmentioning

confidence: 99%

Human detection in surveillance videos and its applications - a review

Paul

Haque

Chakraborty

2013

EURASIP J. Adv. Signal Process.

157

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“…In order to improve the effectiveness of control and management of crowd safety, crowd simulation, surveillance and related analytics have been attracting increasing attention in the field of computer science [2]. Crowd surveillance usually includes crowd detection, counting and analysis [3][4][5]. There are two main categories of approaches in solving the problem of crowd detection and counting.…”

Section: Introductionmentioning

confidence: 99%

Flow field texture representation-based motion segmentation for crowd counting

Zhang

Cao

et al. 2015

Machine Vision and Applications

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It is evident that crowd counting is one of bottlenecks for crowd-related computer vision theory and applications such as surveillance. Since accuracy of estimating crowd size dominantly depends on the performance of motion detection of pedestrians, this paper attacks the challenging problem mainly by proposing a motion segmentation method based on flow field texture representation. Firstly, the motion crowd and background are represented as different texture images by employing line integral convolution. Then information entropy is introduced to quantify the textures as different values so that the texture images can be segmented; further an optimal threshold is obtained via Otsu method to segment the binarization entropy image. Finally, the area of motion foreground pixels is calculated for each image in a crowd motion video. The size of the crowd is estimated by least squares fitting using abundant datum of foreground pixels' area and the number of individuals in a crowd. Experimental results demonstrate that the proposed crowd counting method outperforms background subtraction, Gaussian mixture model and optical flow-based methods in terms of mean absolute error and mean relative error.

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“…A number of studies create models of the body or head and use these models to match similar objects in detected images [1,2]. Other studies use classifiers for head or human body detection, by which numerous samples are collected and useful features are extracted for classifier training [3,4]. These approaches for crowd counting cannot only count the number of people, but also determine the positions of all detected people.…”

Section: Introductionmentioning

confidence: 99%