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Neural Computing and Applications

Erschienen in:

01.06.2014 | Original Article

Situation recognition using image moments and recurrent neural networks

verfasst von: Yaser Daanial Khan, Farooq Ahmed, Sher Afzal Khan

Erschienen in: Neural Computing and Applications | Ausgabe 7-8/2014

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Abstract

Surveillance is an important need for a secured and supervised environment. Manual supervision for the purpose of surveillance proves to be expensive and prone to slipups. Many researchers have worked to provide an automated solution to this problem. In this article, we present a solution to this problem using image moments and recurrent neural networks. For this purpose, frames are first extracted from a live video and the foreground of the frame is sieved out while the background is discarded. Feature vectors are obtained for each frame after computing raw, central, scale-invariant and rotation-invariant moments of the images. These vectors are used to train and ultimately simulate a recurrent neural network. The results generated from this model exhibit an accuracy of 96.4 % in identification of events within consecutive frames.

Vorheriger Artikel Two-dimensional complete neighborhood preserving embedding

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Titel: Situation recognition using image moments and recurrent neural networks
verfasst von: Yaser Daanial Khan
Farooq Ahmed
Sher Afzal Khan
Publikationsdatum: 01.06.2014
Verlag: Springer London
Erschienen in: Neural Computing and Applications / Ausgabe 7-8/2014
Print ISSN: 0941-0643
Elektronische ISSN: 1433-3058
DOI: https://doi.org/10.1007/s00521-013-1372-4

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