Water Filling: Unsupervised People Counting via Vertical Kinect Sensor

Zhang, Xucong; Yan, Junjie; Feng, Shikun; Lei, Zhen; Dong, Yi; Li, Stan Z.

doi:10.1109/avss.2012.82

Cited by 83 publications

(38 citation statements)

References 20 publications

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“…Recent years, research on pedestrian detection and counting has made great progress: [1,2] use regression techniques to learn a map between features and the number of people in the training set and then use the map to estimate the number of people in novel test images or videos; In [3,4], multi-scale windows slide over the whole image and a binary classifier is adopted to determine whether there is a people within the window; literature [5] put forward a method based on facial feature description and SVM (support vector machine) to count pedestrians; literature [6] adopts background subtraction based on threshold to extract object information, then uses connected component detection algorithm, setting the object feature and shape judgment condition and marking object region, finally count the number of people, but it can't remove the influences by some problem such as illumination, rapidly changing weather conditions, people head which are covered completely etc. ; literature [7] construct a novel system that uses vertical Kinect sensor for people counting which equals to find the suitable local minimum regions, then propose a novel unsupervised water filling method that can find these regions with the property of robustness, but it can't handle the situation where some moving object is closer to the sensor than head; literature [8] uses Bayesian Gaussian process to learn the map between holistic features and the number of people. In [9], KLT tracker and agglomerative clustering were used.…”

Section: Introductionmentioning

confidence: 99%

Pedestrian Detection and Counting Based on Ellipse Fitting and Object Motion Continuity for Video Data Analysis

Wang

Zhang

2015

Intelligent Computing Theories and Methodologies

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Abstract. In order to detect and count pedestrians in different kinds of scenes, this paper put forward a method of solving the problem on video sequences captured from a fixed camera. After preprocessing operations on the original video sequences (Gaussian mixture modeling, three-frame-differencing, image binaryzation, Gaussian filtering, dilation and erosion) we extract the relatively complete pedestrian contours. Then we use the least square ellipse fitting method on those contours that has been extracted, the center of the ellipse is undoubtedly regarded as the tracking point of a pedestrian. With those points, a pedestrian matching pursuit and counting algorithm based on object motion continuity is used for tracking and counting pedestrians, this method can be better used in those scenes which are sparse and rarely obscured. Experiments validate that our pedestrian matching pursuit and counting algorithm has obvious superiorities: good real-time performance and high accuracy.

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Section: Introductionmentioning

confidence: 99%

Pedestrian Detection and Counting Based on Ellipse Fitting and Object Motion Continuity for Video Data Analysis

Wang

Zhang

2015

Intelligent Computing Theories and Methodologies

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“…The Detection+Counting approach uses a pedestrian detector to identify candidate person regions in an image, then typically applies some type of segmentation or disambiguation post-processing to verify person candidates before counting. Recent counting-by-detection approaches use either RGB [4,2] or depth imagery [14,7,9].…”

Section: Introductionmentioning

confidence: 99%

“…The simplest way of exploiting depth for counting is to orient cameras perpendicularly to the ground plane [14]. This approach is simple and effective, but poses a strong constraint on system deployment.…”

Section: Introductionmentioning

confidence: 99%

Real-time people counting from depth imagery of crowded environments

Bondi

Seidenari

Bagdanov

et al. 2014

2014 11th IEEE International Conference on Advanced Video and Signal Based Surveillance (AVSS)

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In this paper we describe a system for automatic people counting in crowded environments. The approach we propose is a counting-by-detection method based on depth imagery. It is designed to be deployed as an autonomous appliance for crowd analysis in video surveillance application scenarios. Our system 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. The system runs in realtime, at a frame-rate of about 20 fps. We collected a dataset of RGB-D sequences representing three typical and challenging surveillance scenarios, including crowds, queuing and groups. An extensive comparative evaluation is given between our system and more complex, Latent SVM-based head localization for person counting applications.

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“…When the sensor faces downwards, only the tops of passengers' heads are visible and related features can be identified and tracked. The ability of these sensors to count pedestrians was only recently demonstrated (Zhang et al, 2012) and tested (Del Pizzo et al, 2016;Vera et al, 2016). In contrast, pedestrian sensing by using visible light is better established.…”

Section: Introductionmentioning

confidence: 99%

Pedestrian monitoring techniques for crowd-flow prediction

Martani

StentSimon

AcikgozSinan

et al. 2017

Proceedings of the Institution of Civil Engineers - Smart Infra

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The high concentration and flow rate of people in train stations during rush hours can pose a prominent risk to passenger safety and comfort. In situ counting systems are a critical element for predicting pedestrian flows in real time, and their capabilities must be rigorously tested in live environments. The focus of this paper is on evaluating the reliability of two alternative counting systems, the first using an array of infrared depth sensors and the second a visible light (RGB) camera. Both proposed systems were installed at a busy walkway in London Bridge station. The data were collected over a period of 2 months, after which, portions of the data set were labelled for quantitative evaluation against ground truth. In this paper, the implementation of the two different counting technologies is described, and the accuracy and limitations of both approaches under different conditions are discussed. The results show that the developed RGB-based system performs reliably across a wide range of conditions, while the depth-based approach proves to be a useful complement in conditions without significant ambient sunlight, such as underground passageways.

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Water Filling: Unsupervised People Counting via Vertical Kinect Sensor

Abstract: Abstract

Cited by 83 publications

References 20 publications

Pedestrian Detection and Counting Based on Ellipse Fitting and Object Motion Continuity for Video Data Analysis

Pedestrian Detection and Counting Based on Ellipse Fitting and Object Motion Continuity for Video Data Analysis

Real-time people counting from depth imagery of crowded environments

Pedestrian monitoring techniques for crowd-flow prediction

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