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Paper TuC08.6

Utsumi, Yuzuko (Osaka Prefecture University), Sommerlade, Eric (University of Oxford), Bellotto, Nicola (University of Lincoln), Reid, Ian (University of Oxford)

Cognitive Active Vision for Human Identification

Scheduled for presentation during the Regular Session "Human Detection and Tracking" (TuC08), Tuesday, May 15, 2012, 15:45−16:00, Meeting Room 8 (Wacipi)

2012 IEEE International Conference on Robotics and Automation, May 14-18, 2012, RiverCentre, Saint Paul, Minnesota, USA

This information is tentative and subject to change. Compiled on December 11, 2017

Keywords Surveillance Systems, Human Detection & Tracking, AI Reasoning Methods

Abstract

We describe an integrated, real-time multi-camera surveillance system that is able to find and track individuals, acquire and archive facial image sequences, and perform face recognition. The system is based around an inference engine that can extract high-level information from an observed scene, and generate appropriate commands for a set of pan-tiltzoom (PTZ) cameras. The incorporation of a reliable facial recognition into the high-level feedback is a main novelty of our work, showing how high-level understanding of a scene can be used to deploy PTZ sensing resources effectively. The system comprises a distributed camera system using SQL tables as virtual communication channels, Situation Graph Trees for knowledge representation, inference and high-level camera control, and a variety of visual processing algorithms including an on-line acquisition of facial images, and on-line recognition of faces by comparing image sets using subspace distance. We provide an extensive evaluation of this method using our system for both acquisition of training data, and later recognition. A set of experiments in a surveillance scenario show the effectiveness of our approach and its potential for real applications of cognitive vision.

 

 

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