Cited By
View all- Katsamenis IBakalos NKarolou EDoulamis ADoulamis N(2022)Fall Detection Using Multi-Property Spatiotemporal Autoencoders in Maritime EnvironmentsTechnologies10.3390/technologies1002004710:2(47)Online publication date: 29-Mar-2022
Man Overboard: Fall detection using spatiotemporal convolutional autoencoders in maritime environments
Pages 420 - 425
Abstract
Man overboard incidents in a maritime vessel are serious accidents where, the efficient and rapid detection is crucial in the recovery of the victim. The severity of such accidents, urge the use of intelligent systems that are able to automatically detect a fall and provide relevant alerts. To this end the use of novel deep learning and computer vision algorithms have been tested and proved efficient in problems with similar structure. This paper presents the use of a deep learning framework for automatic detection of man overboard incidents. We investigate the use of simple RGB video streams for extracting specific properties of the scene, such as movement and saliency, and use convolutional spatiotemporal autoencoders to model the normal conditions and identify anomalies. Moreover, in this work we present a dataset that was created to train and test the efficacy of our approach.
References
[1]
E. Örtlund and M. Larsson, “Man Overboard detecting systems based on wireless technology,” 2018.
[2]
Sevin, C. BAYILMIŞ, İ. ERTÜRK, H. EKİZ, and A. Karaca, “Design and implementation of a man-overboard emergency discovery system based on wireless sensor networks,” Turk. J. Electr. Eng. Comput. Sci., vol. 24, no. 3, pp. 762–773, 2016.
[3]
I. Katsamenis, E. Protopapadakis, A. S. Voulodimos, D. Dres, and D. Drakoulis. "Man overboard event detection from rgb and thermal imagery: Possibilities and limitations," In Proceedings of the 13th ACM International Conference on PErvasive Technologies Related to Assistive Environments, pp. 1-6. 2020.
[4]
A. S. Voulodimos, D. I. Kosmopoulos, N. D. Doulamis, and T. A. Varvarigou, “A top-down event-driven approach for concurrent activity recognition,” Multimed. Tools Appl., vol. 69, no. 2, pp. 293–311, Mar. 2014.
[5]
N. D. Doulamis, A. S. Voulodimos, D. I. Kosmopoulos, and T. A. Varvarigou, “Enhanced Human Behavior Recognition Using HMM and Evaluative Rectification,” in Proceedings of the First ACM International Workshop on Analysis and Retrieval of Tracked Events and Motion in Imagery Streams, New York, NY, USA, 2010, pp. 39–44.
[6]
K. Makantasis, E. Protopapadakis, A. Doulamis, N. Doulamis, and N. Matsatsinis, “3D measures exploitation for a monocular semi-supervised fall detection system,” Multimed. Tools Appl., vol. 75, no. 22, pp. 15017–15049, Nov. 2016.
[7]
C. Rougier, J. Meunier, A. St-Arnaud, and J. Rousseau, “Robust video surveillance for fall detection based on human shape deformation,” IEEE Trans. Circuits Syst. Video Technol., vol. 21, no. 5, pp. 611–622, 2011.
[8]
M. Yu, A. Rhuma, S. M. Naqvi, L. Wang, and J. Chambers, “A posture recognition-based fall detection system for monitoring an elderly person in a smart home environment,” IEEE Trans. Inf. Technol. Biomed., vol. 16, no. 6, pp. 1274–1286, 2012.
[9]
S. Lee, H. G. Kim and Y. M. Ro, "BMAN: Bidirectional Multi-Scale Aggregation Networks for Abnormal Event Detection," IEEE Trans. on Image Proc., vol. 29, pp. 2395-2408, 2020.
[10]
A. S. Voulodimos, N.D. Doulamis, D.I. Kosmopoulos, and T.A. Varvarigou, “Improving multi-camera activity recognition by employing neural network based readjustment,” Applied Artificial Intelligence, 26(1-2), 97-118, 2012.
[11]
N. Bakalos, "Protecting water infrastructure from cyber and physical threats: Using multimodal data fusion and adaptive deep learning to monitor critical systems." IEEE Signal Processing Magazine, 36.2, pp. 36-48, 2019.
[12]
S. Wan, X. Xu, T. Wang and Z. Gu, "An Intelligent Video Analysis Method for Abnormal Event Detection in Intelligent Transportation Systems," IEEE Trans. on Intell. Transportation Systems, (to be published)
[13]
R. Leyva, V. Sanchez and C. Li, "Fast Detection of Abnormal Events in Videos with Binary Features," IEEE ICASSP, Calgary, AB, pp. 1318-1322, 2018.
[14]
K.-W. Cheng, Y.-T. Chen, and W.-H. Fang, “Video anomaly detection and localization using hierarchical feature repre- sentation and Gaussian process regression,” IEEE CVPR, pp. 2909–2917, 2015.
[15]
C. Lu, J. Shi, and J. Jia, “Abnormal Event Detection at 150 FPS in MATLAB,” IEEE ICCV, pages 2720– 2727, 2013.
[16]
H. Ren, W. Liu, S. I. Olsen, S. Escalera, and T. B. Moes- lund, “Unsupervised Behavior-Specific Dictionary Learning for Abnormal Event Detection,” Proc. of BMVC, pp. 28.1–28.13, 2015.
[17]
M. Hasan, J. Choi, J. Neumann, A. K. Roy-Chowdhury, and L. S. Davis, “Learning temporal regularity in video sequences,” IEEE CVPR, pages 733–742, 2016.
[18]
Xu Dan, Ricci Elisa, Yan Yan, Song Jingkuan and Sebe Nicu, "Learning Deep Representations of Appearance and Motion for Anomalous Event Detection", BMVC, 2015.
[19]
L. Wang, F. Zhou, Z. Li, W. Zuo and H. Tan, "Abnormal Event Detection in Videos Using Hybrid Spatio-Temporal Autoencoder," 25th IEEE International Conference on Image Processing (ICIP), Athens, 2018, pp. 2276-2280, 2018.
[20]
A. Del Giorno, J. Bagnell, and M. Hebert, “A Discrimina- tive Framework for Anomaly Detection in Large Videos,” Proc. of ECCV, pp. 334–349, 2016.
[21]
J. K. Dutta and B. Banerjee, “Online Detection of Abnormal Events Using Incremental Coding Length,” In Proceedings of AAAI, pages 3755–3761, 2015.
[22]
R.T. Ionescu, S. Smeureanu, B. Alexe, and M. Popescu, “Un-masking the abnormal events in video,” IEEE ICCV, pp. 2895–2903, 2017.
[23]
S. Smeureanu, R. T. Ionescu, M. Popescu, and B. Alexe, “Deep Appearance Features for Abnormal Behavior Detection in Video,” In Proceedings of ICIAP, Volume 10485, pages 779–789, 2017.
[24]
Y. Liu, C.-L. Li, and B. Poczos, “Classifier Two-Sample Test for Video Anomaly Detections,” In Proceedings of BMVC, 2018.
[25]
X. Mo, V. Monga, R. Bala, and Z. Fan, “Adaptive sparse representations for video anomaly detection,” IEEE Trans. Circuits Syst. Video Technol., vol. 24, no. 4, pp. 631–645, Apr. 2014.
[26]
F. Jiang, Y. Wu, and A. K. Katsaggelos, “A dynamic hierarchical clustering method for trajectory-based unusual video event detection,” IEEE Trans. Image Process., vol. 18, no. 4, pp. 907–913, Apr. 2009.
[27]
L. Li, K. Jamieson, G. DeSalvo, A. Rostamizadeh, and A. Talwalkar, “Hyperband: A novel bandit-based approach to hyperparameter optimization,” The Journal of Machine Learning Research, 18(1), pp.6765-6816, 2016.
[28]
Kaselimi, Maria, "Bayesian-optimized bidirectional LSTM regression model for non-intrusive load monitoring." ICASSP 2019-2019 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP). IEEE, 2019
Recommendations
Man overboard event detection from RGB and thermal imagery: possibilities and limitations
PETRA '20: Proceedings of the 13th ACM International Conference on PErvasive Technologies Related to Assistive EnvironmentsA man overboard is an emergency incident, in which fast detection is the most crucial factor, for the quickest and most efficient recovery of the victim. As such, efficient monitoring methodologies should be employed. A variety of sensors is available ...
Using simulation to measure the performance of a search operation for a man overboard
ANSS '18: Proceedings of the Annual Simulation SymposiumA man overboard is defined as a situation in which a person falls out at sea from a ship and is in need of rescue. In such cases, a quick and effective search and rescue (SAR) operation are crucial to increase the survival probability of the victim. If ...
MOBDrone: A Drone Video Dataset for Man OverBoard Rescue
Image Analysis and Processing – ICIAP 2022AbstractModern Unmanned Aerial Vehicles (UAV) equipped with cameras can play an essential role in speeding up the identification and rescue of people who have fallen overboard, i.e., man overboard (MOB). To this end, Artificial Intelligence techniques can ...
Comments
Please enable JavaScript to view thecomments powered by Disqus.Information & Contributors
Information
Published In
Copyright © 2021 ACM.
Permission to make digital or hard copies of all or part of this work for personal or classroom use is granted without fee provided that copies are not made or distributed for profit or commercial advantage and that copies bear this notice and the full citation on the first page. Copyrights for components of this work owned by others than ACM must be honored. Abstracting with credit is permitted. To copy otherwise, or republish, to post on servers or to redistribute to lists, requires prior specific permission and/or a fee. Request permissions from [email protected]
Publisher
Association for Computing Machinery
New York, NY, United States
Publication History
Published: 29 June 2021
Check for updates
Author Tags
Qualifiers
- Research-article
- Research
- Refereed limited
Funding Sources
- RESEARCH ? CREATE ? INNOVATE
Conference
PETRA '21
PETRA '21: The 14th PErvasive Technologies Related to Assistive Environments Conference
June 29 - July 2, 2021
Corfu, Greece
Contributors
Other Metrics
Bibliometrics & Citations
Bibliometrics
Article Metrics
- View Citations1Total Citations
- 115Total Downloads
- Downloads (Last 12 months)14
- Downloads (Last 6 weeks)2
Reflects downloads up to 01 Jan 2025
Other Metrics
Citations
Cited By
View all- Katsamenis IBakalos NKarolou EDoulamis ADoulamis N(2022)Fall Detection Using Multi-Property Spatiotemporal Autoencoders in Maritime EnvironmentsTechnologies10.3390/technologies1002004710:2(47)Online publication date: 29-Mar-2022
View Options
Login options
Check if you have access through your login credentials or your institution to get full access on this article.
Sign inFull Access
View options
View or Download as a PDF file.
PDFeReader
View online with eReader.
eReaderHTML Format
View this article in HTML Format.
HTML Format