• Journal of Korea Multimedia Society
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• v.8 no.10
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• pp.1314-1321
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• 2005

In video surveillance and monitoring (VSAM), extracting foreground by detecting moving regions is the most fundamental step. The foreground extracted, however, includes not only objects in motion but also their shadows, which may cause errors in following video image processing steps. To remove the shadows, this paper presents a new technique to determine shadow pixels in the foreground image of a VSAM camera system. The proposed technique utilizes a fact that the effect of shadowing to each pixel is different defending on its brightness in a background image when determining shadow pixels unlike existing techniques where unified decision criteria are used to all pixels. Such an approach can easily accommodate local features in an image and hold consistent Performance even in changing environment. In real experiments, the proposed technique showed better results compared with an existing technique.

• Journal of Sensor Science and Technology
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• v.11 no.1
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• pp.28-38
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• 2002

People are important targets for video surveillance and monitoring(VSAM) but difficult to be analyzed. In this paper, a technique to count people in image sequences is dealt as a prerequisite procedure for automatic tracking and behaviour analysis. A group of people is divided at local minima of the line connecting the highest pixels on the binary image of the people extracted from the image taken by a stationary video camera. As the properties of the divided regions vary according to the relative positions of the people in a group, different states are assigned for the completely occluded, partially occluded, completed separated individual, and wrongly divided regions. By analyzing the transition of the states of divided regions, the number of people on the site monitored is estimated. The technique is checked in real experimental situations.

• Journal of Institute of Control, Robotics and Systems
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• v.15 no.5
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• pp.505-510
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• 2009

In recent years the space where a robot works has been expanding to the human space unlike traditional industrial robots that work only at fixed positions apart from humans. A human in the recent situation may be the owner of a robot or the target in a robotic application. This paper deals with the latter case; when a robot vision system is employed to monitor humans for a surveillance application, each person in a scene needs to be identified. Humans, however, often move together, and occlusions between them occur frequently. Although this problem has not been seriously tackled in relevant literature, it brings difficulty into later image analysis steps such as tracking and scene understanding. In this paper, a probabilistic neural network is employed to learn the patterns of the best dividing position along the top pixels of an image region of partly occlude people. As this method uses only shape information from an image, it is simple and can be implemented in real time.