Fall Detection Using Multi-Property Spatiotemporal Autoencoders in Maritime Environments

• Published in: Technologies (Basel) Vol. 10; no. 2; p. 47
• Main Authors: Katsamenis, Iason, Bakalos, Nikolaos, Karolou, Eleni Eirini, Doulamis, Anastasios, Doulamis, Nikolaos
• Format: Journal Article
• Language: English
• Published: Basel MDPI AG 01.04.2022
• Subjects: Algorithms; Anomalies; computer vision; convolutional autoencoder; Datasets; Deep learning; Fall detection; Machine learning; man overboard; Neural networks; Sensors; spatiotemporal data; Surveillance; Surveillance systems; unsupervised learning; Video data
• DOI: 10.3390/technologies10020047
• ISSN: 2227-7080

Man overboard is an emergency in which fast and efficient detection of the critical event is the key factor for the recovery of the victim. Its severity urges the utilization of intelligent video surveillance systems that monitor the ship’s perimeter in real time and trigger the relative alarms that initiate the rescue mission. In terms of deep learning analysis, since man overboard incidents occur rarely, they present a severe class imbalance problem, and thus, supervised classification methods are not suitable. To tackle this obstacle, we follow an alternative philosophy and present a novel deep learning framework that formulates man overboard identification as an anomaly detection task. The proposed system, in the absence of training data, utilizes a multi-property spatiotemporal convolutional autoencoder that is trained only on the normal situation. We explore the use of RGB video sequences to extract specific properties of the scene, such as gradient and saliency, and utilize the autoencoders to detect anomalies. To the best of our knowledge, this is the first time that man overboard detection is made in a fully unsupervised manner while jointly learning the spatiotemporal features from RGB video streams. The algorithm achieved 97.30% accuracy and a 96.01% F1-score, surpassing the other state-of-the-art approaches significantly.