Dr. Christopher S. Madden Profile

Dr. Christopher S. Madden

at Metro Trains Melbourne

SPIE Involvement:

Author

Area of Expertise:

Video Surveillance , Computer Vision , Human Machine Interfaces , Vitual Worlds , Signature Evaluation , Robotics

Publications (3)

Proceedings Article | 9 October 2018 Presentation + Paper

Methods for measuring time to detect in human observer trials

Joanne Culpepper, Vivienne Wheaton, Christopher Madden, Luke Simmonds

Proceedings Volume 10794, 107940C (2018) https://doi.org/10.1117/12.2325057

KEYWORDS: Video, Target detection, Sensors, Camouflage, Digital photography

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Evaluating the signature of operational platforms has long been a focus of military research. Human observations of targets in the field are perceived to be the most accurate way to assess a target's visible signature, although the results are limited to observers present in the field. Field observations do not introduce image capture or display artefacts, nor are they completely static, like the photographs used in screen based human observation experiments. A number of papers provide advances in the use of photographs and imagery to estimate the detectability of military platforms; however few describe advances in conducting human observer field trials.

This paper describes the conduct of a set of human field observation trials for detecting small maritime crafts in a littoral setting. This trial was conducted from the East Arm Port in Darwin in February 2018 with up to 6 observers at a time and was used to investigate incremental improvements to the observation process compared to small craft trials conducted in 2013. This location features a high number of potential distractors, which make it more difficult to find the small target crafts. The experimental changes aimed to test ways to measure time to detect, a result not measured at the previous small craft detection experiment, through the use of video monitoring of the observation line to compare with the use of observer-operated stop watches. This experiment also included the occasional addition of multiple targets of interest in the field of regard. Initial analysis of time-to-detect data indicates the video process may accurately assess the time to detect targets by the observers, but only if observers are effectively trained. Ideas on how to further automate the process for the human observer task are also described; however this system has yet to be implemented. This improved human observer trial process will assist the development of signature assessment models by obtaining more accurate data from field trials, including targets moving through a dynamic scene.

Proceedings Article | 5 October 2017 Paper

Comparing synthetic imagery with real imagery for visible signature analysis: human observer results

Joanne Culpepper, Noel Richards, Christopher Madden, Neal Winter, Vivienne Wheaton

Proceedings Volume 10432, 104320H (2017) https://doi.org/10.1117/12.2277580

KEYWORDS: Target detection, 3D acquisition, 3D image processing, 3D modeling, Visualization, Digital photography, Vegetation, Visual analytics, Photography, Airborne remote sensing

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Proceedings Article | 24 October 2016 Presentation + Paper

Utilising E-on Vue and Unity 3D scenes to generate synthetic images and videos for visible signature analysis

Christopher Madden, Noel Richards, Joanne Culpepper

Proceedings Volume 9997, 99970P (2016) https://doi.org/10.1117/12.2240811

KEYWORDS: Target acquisition, Target detection, 3D image processing, Received signal strength, 3D acquisition, Video, Photography, Light sources and illumination, 3D modeling, Clouds

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