  Gun violence is a critical security problem, and it is imperative for the
computer vision community to develop effective gun detection algorithms for
real-world scenarios, particularly in Closed Circuit Television (CCTV)
surveillance data. Despite significant progress in visual object detection,
detecting guns in real-world CCTV images remains a challenging and
under-explored task. Firearms, especially handguns, are typically very small in
size, non-salient in appearance, and often severely occluded or
indistinguishable from other small objects. Additionally, the lack of
principled benchmarks and difficulty collecting relevant datasets further
hinder algorithmic development. In this paper, we present a meticulously
crafted and annotated benchmark, called \textbf{CCTV-Gun}, which addresses the
challenges of detecting handguns in real-world CCTV images. Our contribution is
three-fold. Firstly, we carefully select and analyze real-world CCTV images
from three datasets, manually annotate handguns and their holders, and assign
each image with relevant challenge factors such as blur and occlusion.
Secondly, we propose a new cross-dataset evaluation protocol in addition to the
standard intra-dataset protocol, which is vital for gun detection in practical
settings. Finally, we comprehensively evaluate both classical and
state-of-the-art object detection algorithms, providing an in-depth analysis of
their generalizing abilities. The benchmark will facilitate further research
and development on this topic and ultimately enhance security. Code,
annotations, and trained models are available at
https://github.com/srikarym/CCTV-Gun.
