Assessment of Data Augmentation Techniques for Firearm Detection in Surveillance Videos

In this paper, we propose a Faster R-CNN based model for detection of firearms in surveillance and Closed-Circuit Television (CCTV) video. Due to the lack of a publicly available benchmark surveillance video database containing firearms, we created our own firearm database composed of only real surveillance footage from scenes with people holding firearms. Portable firearms such as rifles and pistols, often referred to as small arms, have a high degree of variation in surveillance footage in terms of shape, illumination, scale, pose, and occlusion. To deploy a fast and accurate firearm detection system for real world scenarios, it is important to detect all types of common small arms. To accomplish this, we collected images for our database that contain both handguns (pistols and revolvers) and long guns (rifles and shotguns) to capture as much variation of commonly used small arms as possible. To train our firearm detector we first, assess 18 common geometric and photometric data augmentation techniques in order to identify which ones improve detection performance. Next, we identify deep learning based small arm detection techniques that improve detection performance. We reuse these techniques in another more comprehensive augmentation assessment in order to identify the most efficient combination in terms of detection performance. Finally, we optimize the hyper-parameters of all the tested firearm detection models and apply cross-validation. Our proposed model can accurately detect firearms in video frames taken from real surveillance footage close to real-time, yielding precision and recall scores of 93.9% and 96.4% for handguns, and 95.2% and 94.6% for long guns.