Detection and Localization of Firearm Carriers in Complex Scenes for Improved Safety Measures
Arif Mahmood, Abdul Basit, M. Akhtar Munir, Mohsen Ali

TL;DR
This paper introduces a novel method for detecting and localizing firearm carriers in complex scenes by leveraging human-firearm interaction cues, attention mechanisms, and saliency constraints, significantly improving accuracy over existing methods.
Contribution
The paper presents a new approach combining attention and saliency-driven locality preservation for firearm detection, outperforming previous methods on a new dataset.
Findings
Achieved AP of 77.8% on firearm detection
Outperformed baseline with AP of 63.1%
Demonstrated effectiveness of attention and saliency mechanisms
Abstract
Detecting firearms and accurately localizing individuals carrying them in images or videos is of paramount importance in security, surveillance, and content customization. However, this task presents significant challenges in complex environments due to clutter and the diverse shapes of firearms. To address this problem, we propose a novel approach that leverages human-firearm interaction information, which provides valuable clues for localizing firearm carriers. Our approach incorporates an attention mechanism that effectively distinguishes humans and firearms from the background by focusing on relevant areas. Additionally, we introduce a saliency-driven locality-preserving constraint to learn essential features while preserving foreground information in the input image. By combining these components, our approach achieves exceptional results on a newly proposed dataset. To handle…
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Taxonomy
TopicsAnomaly Detection Techniques and Applications · Adversarial Robustness in Machine Learning · Gait Recognition and Analysis
MethodsAverage Pooling
