Robust firearm detection based on learning prior distributions on adversarial autoencoders

• Proposed unsupervised firearm detection framework based on adversarial autoencoders. • Pose data and visual gun features are fused to enhance adversarial model training. • Firearm anomaly scores use reconstruction error based on prior-matching probability. • Early and late fusion strategies are evaluated for improved firearm detection. Detecting anomalies in video surveillance, particularly for firearm detection, remains a critical challenge in public safety systems. Traditional methods often rely on human operators manually monitoring surveillance feeds, which is both inefficient and prone to error. Recent advances in deep learning (DL) offer a promising alternative by enabling models to identify anomalous events without relying on rare and difficult-to-obtain positive samples during training. In this paper, we propose an unsupervised firearm detection framework based on adversarial autoencoders (AAE) networks. By learning a robust representation of normal (negative) training data, the model is able to identify deviations indicative of firearm-related anomalies during inference. Anomaly scores for firearm detection are calculated using reconstruction errors, based on the probability that the test sample aligns with the prior distribution. Our approach enhances the interpretability of firearm related anomaly detection (AD) and demonstrates superior performance on benchmark firearm datasets. Experimental results demonstrate that the proposed method surpasses current state-of-the-art techniques by effectively identifying out-of-distribution (OOD) events in video frames, leveraging learned priors within the AAE architecture. Experimental results on benchmark firearm datasets, including VISILAB, UCF-Firearm, and YouTube demonstrate the effectiveness of our firearm detection approach, achieving an average precision (AP) of 95.2% and an average detection accuracy (ACC) of 95.6%.