A New Swarm Intelligence Technique of Artificial Haemostasis System for Suspicious Person Detection with Visual Result Mining

Abstract

In the last few years, the video surveillance system is ubiquitous and can be found in many sectors (banking, transport, industry) or living areas (cities, office building, and store). Unfortunately, this technology has several drawbacks such as the violation of individual freedom and the inability to prevent malicious acts (stealing, crime, and terrorist attack ... etc.). The authors' work deals on the development of a new video surveillance system to detect suspicious person based on their gestures instead of their faces, using a new artificial haemostasis system composed of four steps: pre-processing (pre-haemostasis) for digitalization of images using a novel technique of representation called n-gram pixel, and the weighting normalized term frequency; Each image vector passes through three filters: primary detection (primary haemostasis), secondary detection (secondary haemostasis) and the final detection (fibrinolysis), with an identification step (plasminogen activation) to evaluate the malicious degree of the person presents in this image; the results obtained by their system are promising compared to the performance of classical machine learning algorithms (C4.5 and KNN). The authors' system is composed of a visualization tool in order to see the results with more realism using the functionality of zooming and rotating. Their objectives are to help the justice in its investigations and ensure the safety of people and nation.