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New Swarm Intelligence Technique of Artificial Social Cockroaches for Suspicious Person Detection Using N-Gram Pixel with Visual Result Mining

New Swarm Intelligence Technique of Artificial Social Cockroaches for Suspicious Person Detection Using N-Gram Pixel with Visual Result Mining

Hadj Ahmed Bouarara, Reda Mohamed Hamou, Abdelmalek Amine
Copyright: © 2015 |Volume: 6 |Issue: 3 |Pages: 27
ISSN: 1947-8569|EISSN: 1947-8577|EISBN13: 9781466677555|DOI: 10.4018/IJSDS.2015070105
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MLA

Bouarara, Hadj Ahmed, et al. "New Swarm Intelligence Technique of Artificial Social Cockroaches for Suspicious Person Detection Using N-Gram Pixel with Visual Result Mining." IJSDS vol.6, no.3 2015: pp.65-91. http://doi.org/10.4018/IJSDS.2015070105

APA

Bouarara, H. A., Hamou, R. M., & Amine, A. (2015). New Swarm Intelligence Technique of Artificial Social Cockroaches for Suspicious Person Detection Using N-Gram Pixel with Visual Result Mining. International Journal of Strategic Decision Sciences (IJSDS), 6(3), 65-91. http://doi.org/10.4018/IJSDS.2015070105

Chicago

Bouarara, Hadj Ahmed, Reda Mohamed Hamou, and Abdelmalek Amine. "New Swarm Intelligence Technique of Artificial Social Cockroaches for Suspicious Person Detection Using N-Gram Pixel with Visual Result Mining," International Journal of Strategic Decision Sciences (IJSDS) 6, no.3: 65-91. http://doi.org/10.4018/IJSDS.2015070105

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Abstract

In the last decade, surveillance camera technology has become widely practiced in public and private places to ensure the safety of individuals. Merely, face to limits of violation the private life of people and the inability to identify malicious persons that hid their faces, finding a new policy of surveillance video has become compulsory. The authors' work deals on the development of a suspicious person detection system using a new insect behaviour algorithm called artificial social cockroaches ASC based on a new image representation method (n-gram pixel). It has as input a set of artificial cockroaches (human images) to classify them (hide) into shelters (classes) suspicious or normal depending on a set of aggregation rules (shelter darkness, congener's attraction and security quality). Their experiments were performed on a modified MuHAVi dataset and using the validation measures (recall, precision, f-measure, entropy and accuracy), in order to show the benefit derived from using such approach compared to the result of classical algorithms (KNN and C4.5). Finally, a visualisation step was achieved to see the results in graphical form with more realism for the purpose to help policeman, security associations and justice in their investigation.

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