Crowd-Aided Anomaly Detection in Surveillance Videos
Ryuya Itano, Tomoya Nohara, Takahiro Koita · 2022 IEEE International Conference on Big Data (Big Data) · 2022
Surveillance cameras are increasingly being installed to detect public anomalies and prevent crimes. However, such additional surveillance cameras require a larger workforce to monitor videos, inducing heavy costs. Various methods have been proposed to automate anomaly detection in surveillance videos, including deep-learning-based methods. Unfortunately, state-of-the-art methods based on deep learning have not yet achieved a high level of anomaly localization. Our previous study proposed a crowdsourcing-based anomaly detection method which utilizes human cognitive abilities on demand. In an evaluation experiment using shoplifting videos, our method demonstrated high accuracy. In this study, we extended the application of our crowdsourcing-based method to anomaly detection, which is a broader application area. Because a method that relies entirely on crowdworkers is costly, we propose a new crowd-aided anomaly detection method that supports deep-learning-based methods by employing a crowdsourcing-based method, which provides high anomaly localization ability at a low cost.