Video Security for Ambient Intelligence

Snidaro, Lauro; Micheloni, Christian; Chiavedale, C.

doi:10.1109/tsmca.2004.838478

Cited by 69 publications

(36 citation statements)

References 28 publications

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“…Significant headway has been made in tracking individuals through spaces using wireless devices [1][12] [17] and in recognizing activities within the space base on video data [4][6] [14], motion sensor data [8] [16], or other sources of information [10] [11]. However, much of the theory and most of the algorithms are designed to handle one individual in the space at a time.…”

Section: Introductionmentioning

confidence: 99%

Attributing events to individuals in multi-inhabitant environments

Crandall¹,

Cook²

2008

4th International Conference on Intelligent Environments (IE 08)

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Intelligent environment research has resulted in many useful tools such as activity recognition, prediction, and automation. However, most of these techniques have been applied in the context of a single resident. A current looming issue for intelligent environment systems is performing these same techniques when multiple residents are present in the environment. In this paper we investigate the problem of attributing sensor events to individuals in a multiresident intelligent environment. Specifically, we use a naïve Bayesian classifier to identify the resident responsible for a unique sensor event. We present results of experimental validation in a real intelligent workplace testbed and discuss the unique issues that arise in addressing this challenging problem.

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Section: Introductionmentioning

confidence: 99%

Attributing events to individuals in multi-inhabitant environments

Crandall¹,

Cook²

2008

4th International Conference on Intelligent Environments (IE 08)

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“…The main areas include human tracking [3,15,7], automatic human detection [14,9,13], counting [8,12]. It is expensive to hire staff to constantly monitor the feeds, and it is only a very small percentage of the video that contains useful information [5].…”

Section: Related Workmentioning

confidence: 99%

Multiple View Surveillance using Image Registration

SandhyaDevi¹,

Reddy²,

Kumar³

et al. 2014

IJCA

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In the area of activity monitoring, video surveillance was mainly used for monitoring the scene by security personnel. However, due to high increase of unethical activities it is being strongly considered as a must for vigilance authorities, even sometimes for some unique purposes such as exam surveillance. The main theme is to ensure that students take their examinations the right way instead of indulging in unfair methods or malpractices. The proposed system aims to detect suspicious activities which occur in the classroom, thereby ruling out copying. We aim to develop a system that will identify the students from the top view camera using the image registration technique and check to see if he/she indulges in any form of malpractices or suspicious activities during the entire course of an examination. To get the aimed results the whole system has been divided into two modules through which the suspicious activities are classified from the normal activities. The first one includes the image registration technique which is used as a novel approach to determine the position of the student from the top view camera. The second module proposes the usage of object detection and skin detection. Finally we compare the normal frames and the suspicious frames based on their respective threshold values which are generated using the threshold technique. Also the accuracy of the system is checked using SVM classification.

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“…Our system can be installed easily in any public place. In the literature there are various tracking systems [26], many researchers detect and track image features, such as color [15,23], KLT [17], corners [24] or textures [4], to track humans. Sophisticated tracking algorithms have been developed to effectively deal with various situations.…”

Section: Related Workmentioning

confidence: 99%

Real-time tracking of humans and visualization of their future footsteps in public indoor environments

Ozturk

Matsunami

Suzuki

et al. 2011

Multimed Tools Appl

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In this work, an interactive entertainment system which employs multiplehuman tracking from a single camera is presented. The proposed system robustly tracks people in an indoor environment and displays their predicted future footsteps in front of them in real-time. The system is composed of a video camera, a computer and a projector. There are three main modules: tracking, analysis and visualization. The tracking module extracts people as moving blobs by using an adaptive background subtraction algorithm. Then, the location and orientation of their next footsteps are predicted. The future footsteps are visualized by a high-paced continuous display of foot images in the predicted location to simulate the natural stepping of a person. To evaluate the performance, the proposed system was exhibited during a public art exhibition in an airport. People showed surprise, excitement, curiosity. They tried to control the display of the footsteps by making various movements.

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Video Security for Ambient Intelligence

Cited by 69 publications

References 28 publications

Attributing events to individuals in multi-inhabitant environments

Attributing events to individuals in multi-inhabitant environments

Multiple View Surveillance using Image Registration

Real-time tracking of humans and visualization of their future footsteps in public indoor environments

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