Abnormality Detection Using LBP Features and K-Means Labelling based Feed-Forward Neural Network in Video Sequence
Ruchika1, Ravindra Kumar Purwar2

1Ruchika, Research Scholar, USIC&T, Guru Gobind Singh Indraprastha University, Dwarka (Delhi), India

2Ravindra Kumar Purwar, Associate Professor, USIC&T, Guru Gobind Singh Indraprastha University, Dwarka  (Delhi), India.

Manuscript received on 05 August 2019 | Revised Manuscript received on 12 August 2019 | Manuscript Published on 26 August 2019 | PP: 629-633 | Volume-8 Issue-9S August 2019 | Retrieval Number: I11000789S19/19©BEIESP | DOI: 10.35940/ijitee.I1100.0789S19

Open Access | Editorial and Publishing Policies | Cite | Mendeley | Indexing and Abstracting
© The Authors. Blue Eyes Intelligence Engineering and Sciences Publication (BEIESP). This is an open-access article under the CC-BY-NC-ND license (http://creativecommons.org/licenses/by-nc-nd/4.0/)

Abstract: Video surveillance is widely used in various domains like military, commercial and consumer areas. One of the objectives in video surveillance is the detection of normal and abnormal behavior.It has always been a challenge to accurately identify such events in any real time video sequence. In this paper, abnormality detection method using Local Binary Pattern and k-means labeling basedfeed-forward neural network has been proposed. The performance of the proposed method has also been compared with four other techniques in literature to show its worthiness. It can be seen in the experimental results that an accuracy of up to 98% has been achieved for the proposed technique.

Keywords: NN, k-Mean Labeling, Abnormality Detection, Video Surveillance.
Scope of the Article: Residential, Commercial, Industrial and Public Works