AUTOMATIC DETECTION OF LOITERING BEHAVIOUR USING SPATIOTEMPORAL IMAGE PROCESSING
Price
Free (open access)
Transaction
Volume
125
Pages
10
Page Range
133 - 142
Published
2019
Size
609 kb
Paper DOI
10.2495/CMEM190131
Copyright
WIT Press
Author(s)
YUTA EBIHARA, TERUOMI KATORI, TAKASHI IZUMI
Abstract
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”.
Keywords
spatiotemporal image processing, loitering behaviour, staying time