A Method of Video Loitering Detection Based on Dynamic Programming

Min, Chaobo; Zhang, Junju; Xu, Hui; Zhang, Miao; Liu, Feng

doi:10.1109/sopo.2012.6270999

Cited by 3 publications

(3 citation statements)

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“…We use Equation ( 8) to establish an active rectangle frame to determine whether the pedestrian's Through the above method, we can track the pedestrian and get a series of tracking coordinates defined in Equation (6).…”

Section: The Rectangle Loiteringmentioning

confidence: 99%

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Loitering Detection Based on Pedestrian Activity Area Classification

et al. 2019

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Loitering detection can help recognize vulnerable people needing attention and potential suspects harmful to public security. The existing loitering detection methods used time or target trajectories as assessment criteria, and only handled some simple loitering circumstances because of complex track. To solve these problems, this paper proposes a loitering detection method based on pedestrian activity area classification. The paper first gave loitering definition from a new perspective using the size of pedestrian activity area. The pedestrian loitering behaviors were divided into three categories. The proposed algorithms dynamically calculate enclosing rectangle, ellipse, and sector of pedestrian activity areas through curve fitting based on trajectory coordinates within given staying time threshold. The loitering is recognized if the pedestrian activity is detected to be constrained in an area within a certain period of time. The algorithm does not need to calculate complex trajectories. The PETS2007 dataset and our own self-collected simulated test videos were tested. The experimental results show that the proposed method accurately detected the pedestrian loitering, not only detected some loitering that the existing methods could not detect, but also distinguishing different types of loitering. The proposed method is effectiveness, robust, and simplified in implementation.

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Section: The Rectangle Loiteringmentioning

confidence: 99%

“…The demand of video surveillance systems in public places is large [4,5]. Pedestrian loitering detection is mainly applied to safety monitoring in public places such as public transportation areas to ensure the urban safety [6].…”

Section: Introductionmentioning

confidence: 99%

Loitering Detection Based on Pedestrian Activity Area Classification

et al. 2019

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“…In other examples, Bird et al [6] describe how to detect drug dealers loitering at bus stops, using RGB intensity matching. Min et al [7] describe loitering detection using characteristic point tracking.…”

Section: Introductionmentioning

confidence: 99%

Automatic Detection of Loitering Behaviour Using Spatiotemporal Image Processing

Ebihara

Katori

Izumi

2019

Computational Methods and Experimental Measurements XIX

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In this paper, the authors propose a method for detecting loitering behaviour automatically from security camera images acquired in a corridor or passage, and the authors examine the performance of the proposed method. Image sensors (security cameras) are widely used for crime prevention. In this study, for educational settings, the authors developed a system for automatically detecting loitering behaviour where a student is worried about whether he or she is permitted to enter a laboratory on his/her first visit. Using the results, staff in the laboratory can approach them and appropriately guide the student during his or her visit. The purpose of this study is to detect loitering behaviour including fuzzy actions. Detecting loitering behaviour involves the ethical issue of ensuring that the captured images do not infringe an individual's privacy. In addition, there are a number of technical problems: What is a unique characteristic value indicating the target behaviour?; the method should not require much computational power; and it should be possible to explain the reason for the judgment result. In this study, to ensure privacy, the authors avoid using original images, for example, images in which the face or body of an individual can be recognized, and instead the authors use spatiotemporal images. General image processing is highly complex and requires computers using high-performance CPUs and a lot of memory. However, usual video capturing and behaviour recognition are expected to involve lower complexity. Spatiotemporal image processing can solve the technical problems mentioned above, for example, decreasing the computational complexity and maintaining high computational performance. In addition, as a measurement characteristic value, the authors adopt a simple staying time only, and the authors classify the behaviour into only two categories: "loitering behaviour" or "not loitering".

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