Behavior Modeling by Neural Networks

This paper discusses a method for abnormal motion detection and its real-time implementation on a smart camera. Abnormal motion detection is a surveillance technique that only allows unfamiliar motion patterns to result in alarms. Our approach has two phases. First, normal motion is detected and the motion paths are trained, building up a model of normal behaviour. Feed-forward neural networks are here used for learning. Second, abnormal motion is detected by comparing the current observed motion to the stored model. A complete demonstration system is implemented to detect abnormal paths of persons moving in an indoor space. As platform we used a wireless smart camera system containing an SIMD (SingleInstruction Multiple-Data) processor for real-time detection of moving persons and an 8051 microcontroller for implementing the neural network. The 8051 also functions as camera host to broadcast abnormal events using ZigBee to a main network system. 1

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Abnormal motion detection in a real-time smart camera system

Tehrani¹,

Kleihorst

Meijer

et al. 2009

2009 Third ACM/IEEE International Conference on Distributed Smart Cameras (ICDSC)

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Behavior Modeling by Neural Networks

Cited by 1 publication

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Abnormal motion detection in a real-time smart camera system

Abnormal motion detection in a real-time smart camera system

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