Low power multi-camera system and algorithms for automated threat detection

David J. Huber; Deepak Khosla; Yang Chen; Darrel J. Van Buer; Kevin Martin

doi:10.1117/12.2016104

16 May 2013 Low power multi-camera system and algorithms for automated threat detection

David J. Huber, Deepak Khosla, Yang Chen, Darrel J. Van Buer, Kevin Martin

Proceedings Volume 8740, Motion Imagery Technologies, Best Practices, and Workflows for Intelligence, Surveillance, and Reconnaissance (ISR), and Situational Awareness; 874006 (2013) https://doi.org/10.1117/12.2016104
Event: SPIE Defense, Security, and Sensing, 2013, Baltimore, Maryland, United States

Abstract

A key to any robust automated surveillance system is continuous, wide field-of-view sensor coverage and high accuracy target detection algorithms. Newer systems typically employ an array of multiple fixed cameras that provide individual data streams, each of which is managed by its own processor. This array can continuously capture the entire field of view, but collecting all the data and back-end detection algorithm consumes additional power and increases the size, weight, and power (SWaP) of the package. This is often unacceptable, as many potential surveillance applications have strict system SWaP requirements. This paper describes a wide field-of-view video system that employs multiple fixed cameras and exhibits low SWaP without compromising the target detection rate. We cycle through the sensors, fetch a fixed number of frames, and process them through a modified target detection algorithm. During this time, the other sensors remain powered-down, which reduces the required hardware and power consumption of the system. We show that the resulting gaps in coverage and irregular frame rate do not affect the detection accuracy of the underlying algorithms. This reduces the power of an N-camera system by up to approximately N-fold compared to the baseline normal operation. This work was applied to Phase 2 of DARPA Cognitive Technology Threat Warning System (CT2WS) program and used during field testing.

Citation Download Citation

David J. Huber, Deepak Khosla, Yang Chen, Darrel J. Van Buer, and Kevin Martin "Low power multi-camera system and algorithms for automated threat detection", Proc. SPIE 8740, Motion Imagery Technologies, Best Practices, and Workflows for Intelligence, Surveillance, and Reconnaissance (ISR), and Situational Awareness, 874006 (16 May 2013); https://doi.org/10.1117/12.2016104

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