Mr. Benjamin Abruzzo Profile

Benjamin Abruzzo

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Proceedings Article | 12 April 2021 Presentation + Paper

Experimentation for optimization of heterogeneous drone swarm configurations: terrain and distribution

Ross Arnold, Elizabeth Mezzacappa, Melissa Jablonski, Benjamin Abruzzo, Jonathan Jablonski

Proceedings Volume 11746, 1174625 (2021) https://doi.org/10.1117/12.2585589

KEYWORDS: Statistical analysis, Relays, Natural disasters, Computer simulations

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Computer simulation experimentation examined the effectiveness of different Unmanned Aircraft System (UAS) swarm configurations for identification and localization of survivors after a natural disaster using the DroneLab application. Swarms differed in terms of total number of drones and ratio of entities programmed to perform one of three different “personalities”—Relay, Social, and Antisocial. Relay behavior puts a high priority on maintaining proximity to the centroid of the swarm while also maintaining a distance to closest neighbor drones equal to half of the maximum WiFi range. Antisocial drones prioritize an expanding behavior, increasing the spread of the swarm, while the Social behavior prioritizes a contractive behavior resulting in a tighter swarm formation. All drones performed a local waypoint-based search behavior while conducting a spiral-out search pattern upon detecting four or more survivors within a 10-meter radius. Swarm configurations with different ratios of these behaving entities were assessed for mission completion, defined as time to find 90% of the survivors. Mission completions were recorded for four simulation scenarios consisting of two terrains (urban/rural) with two different distributions of survivors (naturalistic/randomized). Ten replications of 98 different drone configurations were evaluated. Statistically significant differences between time to mission completion between the terrains, between the two distributions, and among the iterations were revealed. Qualitative comparisons revealed differences in configurations that performed the best in each terrain. A few configurations performed well in all four scenarios. Moreover, the minimum number of entities needed for well-performing swarms was indicated. The work demonstrates the utility of computer experimentation and statistical analyses for developing a framework for swarm design for operational effectiveness.

Proceedings Article | 21 April 2020 Presentation + Paper

Comparison of skeleton models and classification accuracy for posture-based threat assessment using deep-learning

Kevin Carey, Benjamin Abruzzo, Christopher Lowrance, Eric Sturzinger, Ross Arnold, Christopher Korpela

Proceedings Volume 11413, 1141321 (2020) https://doi.org/10.1117/12.2556422

KEYWORDS: Firearms, Neural networks, Weapons, Image classification, Video

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This paper compares the effectiveness of two different skeletal pose models for a near real-time, multi-stage classifier. A cascaded neural-network (NN) classifier was previously developed to identify the level of threat posed by an armed person based on detected weapons and body posture. On an updated database of images containing armed individuals and groups, AlphaPose was used to calculate both MPII and COCO skeletons while OpenPose was used to calculate the COCO only. For comparison, we evaluated the importance of individual skeletal joints by systematically removing specific joints from the feature vector and retraining a reduced order network. On the database of images, the AlphaPose-COCO network was best able to correctly classify the threat presented by individuals, 83.7% on average, while AlphaPose-MPII registered 82.2% and 77.6% for OpenPose-COCO. As expected, the most important single joint in both skeleton models is the location of the pistol. As a guide for others deciding which skeleton to use for further studies, we conclude that neither skeleton significantly outperforms the other.

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