Detection and Localization of Firearm Carriers in Complex Scenes for Improved Safety Measures

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 inputs of varying sizes, we pass paired human-firearm instances with attention masks as channels through a deep network for feature computation, utilizing an adaptive average pooling layer. We extensively evaluate our approach against existing methods in human-object interaction detection and achieve significant results (AP=77.8\%) compared to the baseline approach (AP=63.1\%). This demonstrates the effectiveness of leveraging attention mechanisms and saliency-driven locality preservation for accurate human-firearm interaction detection. Our findings contribute to advancing the fields of security and surveillance, enabling more efficient firearm localization and identification in diverse scenarios.