Task-oriented situation recognition | Zendy

Alexander Bauer | Zendy; Yvonne Fischer | Zendy

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Task-oriented situation recognition

Author(s) -

Alexander Bauer,

Yvonne Fischer

Publication year - 2010

Publication title -

proceedings of spie, the international society for optical engineering/proceedings of spie

Language(s) - English

Resource type - Conference proceedings

SCImago Journal Rank - 0.192

H-Index - 176

eISSN - 1996-756X

pISSN - 0277-786X

DOI - 10.1117/12.849646

Subject(s) - computer science , task (project management) , video tracking , artificial intelligence , video processing , object detection , computer vision , real time computing , human–computer interaction , pattern recognition (psychology) , management , economics

From the advances in computer vision methods for the detection, tracking and recognition of objects in video streams, new opportunities for video surveillance arise: In the future, automated video surveillance systems will be able to detect critical situations early enough to enable an operator to take preventive actions, instead of using video material merely for forensic investigations. However, problems such as limited computational resources, privacy regulations and a constant change in potential threads have to be addressed by a practical automated video surveillance system. In this paper, we show how these problems can be addressed using a task-oriented approach. The system architecture of the task-oriented video surveillance system NEST and an algorithm for the detection of abnormal behavior as part of the system are presented and illustrated for the surveillance of guests inside a video-monitored building

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