Search NASA⌕ Search

DOE OSTI · 1504598

Procedure for Performing Security Camera Optical Performance Analysis Tests

Abstract

Digital security camera technologies proliferate the security marketplace with manufacturers extoling the functions, features, specifications and virtues of their cameras. A process to comparatively analyze security camera technologies using international camera analysis standards and methods provides a defensible means to assess camera image quality on a technically comparable basis. The procedures in this document use ISO standard camera test charts and camera image analysis software to perform test chart image analysis for comparative camera assessment. Step-by-step procedures for acquiring test chart images in the laboratory and producing analysis data using analysis software GUI controls and interfaces for comparative analysis of security camera image quality are detailed in this document. The procedures have been validated through the test and analysis of fixed box and 180 degree field of view multi-imager cameras as well as, fixed and varifocal lenses and analog-to-digital video encoder image quality performance attributes.

Explore related subjects

Keep this discovery

Explore connections, maps & timelines

BibTeXRIS

Furgal, David T.. 2015-03-01. Procedure for Performing Security Camera Optical Performance Analysis Tests. https://doi.org/10.2172/1504598

Cite the original work for its findings. Save a collection to share your selection of sources.

KEEP EXPLORING

Related reports

Large language models for transportation research: Methodologies, state of the art, and future opportunities

The rapid rise of large language models (LLMs) is transforming transportation research, with significant advancements emerging between 2023 and 2025, a period marked by the inception and swift growth of adopting and adapting LLMs for various transportation applications. Despite these significant advancements, however, a systematic review and synthesis of the existing literature remains lacking. This paper aims to fill this gap by providing a comprehensive review of the methodologies and applications of LLMs in transportation. We explore key applications, including autonomous driving, travel behavior prediction, and general transportation-related queries, alongside LLM methodologies such as zero- or few-shot learning, prompt engineering, and fine-tuning. From the review, critical research gaps are identified. From the methodological perspective, many of the research limitations can be addressed by integrating LLMs with existing tools and refining LLM architectures. From the application perspective, research opportunities for LLMs to address various transportation challenges are also explored. By synthesizing these findings, this review not only presents the state-of-the-art LLM adoption and adaptation in transportation, but also proposes future research directions as well as insights and recommendations for policymakers and practitioners, paving the way for greater LLM-driven research innovations in transportation in the future.

42 ENGINEERING↗