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Deliberate Motion Analytics Fused Radar and Video Test Results Deployed Beyond the Perimeter Fence in a High Noise Environment

Russell, John L.

Security systems that protect the nation’s critical facilities must be capable of detecting physical intrusions in all weather conditions. Intrusion detection sensors in a perimeter with a high nuisance alarm rate (NAR) significantly undermine detection performance and degrade security system effectiveness. This research demonstrated a fused sensor system that can differentiate foliage and weather-induced nuisance alarms from those caused by intruders, providing reliable detection within a two-fence perimeter or beyond the fence. A key element of this work is the creation and application of a “deliberate motion algorithm” that fuses alarm data from radar and video analytics to create video motion detection fused radar system. The two-layer architecture of the algorithm uses machine learning, multi-hypothesis tracking, and Dynamic Bayes Nets to differentiate intruder alarms from weather induced alarms.

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TYPE SAND Report YEAR 2021
DOIOSTI

CHARACTERIZING HUMAN PERFORMANCE: DETECTING TARGETS AT HIGH FALSE ALARM RATES

Proceedings of the 2021 International Topical Meeting on Probabilistic Safety Assessment and Analysis, PSA 2021

Speed, Ann S.; Wheeler, Jason W.; Russell, John L.; Oppel, Fred; Sanchez, Danielle; Silva, Austin R.; Chavez, Anna

The prevalence effect is the observation that, in visual search tasks as the signal (target) to noise (non-target) ratio becomes smaller, humans are more likely to miss the target when it does occur. Studied extensively in the basic literature [e.g., 1, 2], this effect has implications for real-world settings such as security guards monitoring physical facilities for attacks. Importantly, what seems to drive the effect is the development of a response bias based on learned sensitivity to the statistical likelihood of a target [e.g., 3-5]. This paper presents results from two experiments aimed at understanding how the target prevalence impacts the ability for individuals to detect a target on the 1,000th trial of a series of 1000 trials. The first experiment employed the traditional prevalence effect paradigm. This paradigm involves search for a perfect capital letter T amidst imperfect Ts. In a between-subjects design, our subjects experienced target prevalence rates of 50/50, 1/10, 1/100, or 1/1000. In all conditions, the final trial was always a target. The second (ongoing) experiment replicates this design using a notional physical facility in a mod/sim environment. This simulation enables triggering different intrusion detection sensors by simulated characters and events (e.g., people, animals, weather). In this experiment, subjects viewed 1000 “alarm” events and were asked to characterize each as either a nuisance alarm (e.g., set off by an animal) or an attack. As with the basic visual search study, the final trial was always an attack.

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TYPE Conference Presenation YEAR 2021
DOIOSTIScopus
22 Results
22 Results