Cognitive active vision for human identification

Utsumi, Yuzuko and Sommerlade, Eric and Bellotto, Nicola and Reid, Ian (2012) Cognitive active vision for human identification. In: IEEE International Conference on Robotics and Automation (ICRA 2012), 14-18 May 2012, St. Paul, Minnesota, USA.

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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-tilt-zoom (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.

Item Type:Conference or Workshop Item (Paper)
Additional Information: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-tilt-zoom (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.
Keywords:face recognition, active vision, cognitive system
Subjects:G Mathematical and Computer Sciences > G700 Artificial Intelligence
G Mathematical and Computer Sciences > G740 Computer Vision
Divisions:College of Social Science > School of Health & Social Care
ID Code:4836
Deposited By: Nicola Bellotto
Deposited On:04 Jan 2012 00:56
Last Modified:29 Jun 2012 08:28

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