Anomaly Event Retrieval System from TV News and Surveillance Cameras
Pages 953 - 959
Abstract
In an era defined by the proliferation of digital content and a growing reliance on video data, the need for effective anomaly detection systems has never been more pressing. This paper introduces a sophisticated system architecture designed to address the complex challenges associated with acquiring, processing, and presenting anomaly videos. At its core, our architecture prioritizes openness and modularity, allowing for seamless upgrades and customization. This approach ensures adaptability to evolving technology trends and user preferences. We emphasize the crucial aspects of component interfacing and user interaction, highlighting the integration of feedback mechanisms for ongoing system refinement. Additionally, we contribute significantly to the research community by extending an established anomaly event dataset using proven methods and techniques. This extension enhances the dataset’s breadth and depth, providing a valuable resource for training and evaluating anomaly event retrieval systems. Our paper presents a forward-looking system architecture poised to meet the demands of anomaly video detection while also enriching the available resources for anomaly event research. Subsequent sections will delve into architecture components and methodologies, showcasing its potential to revolutionize modern anomaly detection systems.
References
[1]
Shweta Bhardwaj and Mitesh M. Khapra. 2018. I Have Seen Enough: A Teacher Student Network for Video Classification Using Fewer Frames. In CVPR Workshops.
[2]
Shweta Bhardwaj, Mukundhan Srinivasan, and Mitesh M. Khapra. 2019. Efficient Video Classification Using Fewer Frames. 2019 IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR) (2019), 354–363.
[3]
James Davis and Xing Chen. 2003. Calibrating pan-tilt cameras in wide-area surveillance networks. Proceedings Ninth IEEE International Conference on Computer Vision (2003), 144–149 vol.1.
[4]
Zuolin Dong, Jiahong Wei, Xiaoyu Chen, and Pengfei Zheng. 2020. Face Detection in Security Monitoring Based on Artificial Intelligence Video Retrieval Technology. IEEE Access 8 (2020), 63421–63433. https://doi.org/10.1109/ACCESS.2020.2982779
[5]
Yinan Feng, Pan Zhou, Jie Xu, Shouling Ji, and Dapeng Wu. 2019. Video Big Data Retrieval Over Media Cloud: A Context-Aware Online Learning Approach. IEEE Transactions on Multimedia 21, 7 (2019), 1762–1777. https://doi.org/10.1109/TMM.2018.2885237
[6]
Sébastien Frizzi, Rabeb Kaabi, Moez Bouchouicha, Jean-Marc Ginoux, Eric Moreau, and Farhat Fnaiech. 2016. Convolutional neural network for video fire and smoke detection. IECON 2016 - 42nd Annual Conference of the IEEE Industrial Electronics Society (2016), 877–882.
[7]
Mariana-Iuliana Georgescu, Antonio Bărbălău, Radu Tudor Ionescu, Fahad Shahbaz Khan, Marius Claudiu Popescu, and Mubarak Shah. 2020. Anomaly Detection in Video via Self-Supervised and Multi-Task Learning. 2021 IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR) (2020), 12737–12747.
[8]
Shivanand Gornale, Ashvini Babaleshwar, and K Babaleshwar. 2019. Analysis and Detection of Content based Video Retrieval. International Journal of Image, Graphics and Signal Processing 11 (03 2019), 43–57. https://doi.org/10.5815/ijigsp.2019.03.06
[9]
Stephan Hengstler, Daniel Prashanth, Sufen Fong, and Hamid K. Aghajan. 2007. MeshEye: A Hybrid-Resolution Smart Camera Mote for Applications in Distributed Intelligent Surveillance. 2007 6th International Symposium on Information Processing in Sensor Networks (2007), 360–369.
[10]
Nhat Hoang-Xuan, Thang-Long Nguyen-Ho, Cathal Gurrin, and Minh-Triet Tran. 2023. Lifelog Discovery Assistant: Suggesting Prompts and Indexing Event Sequences for FIRST at LSC 2023. In Proceedings of the 6th Annual ACM Lifelog Search Challenge, LSC 2023, Thessaloniki, Greece, June 12-15, 2023, Cathal Gurrin, Björn Þór Jónsson, Duc-Tien Dang-Nguyen, Graham Healy, Jakub Lokoc, Liting Zhou, Luca Rossetto, Minh-Triet Tran, Wolfgang Hürst, Werner Bailer, and Klaus Schoeffmann (Eds.). ACM, 47–52. https://doi.org/10.1145/3592573.3593104
[11]
Maria Tysse Hordvik, Julie Sophie Teilstad Østby, Manoj Kesavulu, Thao-Nhu Nguyen, Tu-Khiem Le, and Duc-Tien Dang-Nguyen. 2023. LifeLens: Transforming Lifelog Search with Innovative UX/UI Design. In Proceedings of the 6th Annual ACM Lifelog Search Challenge, LSC 2023, Thessaloniki, Greece, June 12-15, 2023, Cathal Gurrin, Björn Þór Jónsson, Duc-Tien Dang-Nguyen, Graham Healy, Jakub Lokoc, Liting Zhou, Luca Rossetto, Minh-Triet Tran, Wolfgang Hürst, Werner Bailer, and Klaus Schoeffmann (Eds.). ACM, 1–6. https://doi.org/10.1145/3592573.3593096
[12]
Linjiang Huang, Liang Wang, and Hongsheng Li. 2022. Multi-Modality Self-Distillation for Weakly Supervised Temporal Action Localization. IEEE Transactions on Image Processing 31 (2022), 1504–1519.
[13]
Yoshinari Kameda, Taisuke Takemasa, and Yuichi Ohta. 2004. Outdoor see-through vision utilizing surveillance cameras. Third IEEE and ACM International Symposium on Mixed and Augmented Reality (2004), 151–160.
[14]
Laisong Kang, Shifeng Liu, Hankun Zhang, and Daqing Gong. 2021. Person anomaly detection-based videos surveillance system in urban integrated pipe gallery. Building Research & Information 49, 1 (2021), 55–68.
[15]
Muhammad Numan Khan, Aftab Alam, and Young-Koo Lee. 2020. FALKON: Large-Scale Content-Based Video Retrieval Utilizing Deep-Features and Distributed In-memory Computing. In 2020 IEEE International Conference on Big Data and Smart Computing (BigComp). 36–43. https://doi.org/10.1109/BigComp48618.2020.0-102
[16]
Tu-Khiem Le, Van-Tu Ninh, Mai-Khiem Tran, Graham Healy, Cathal Gurrin, and Minh-Triet Tran. 2022. AVSeeker: An Active Video Retrieval Engine at VBS2022. In MultiMedia Modeling - 28th International Conference, MMM 2022, Phu Quoc, Vietnam, June 6-10, 2022, Proceedings, Part II(Lecture Notes in Computer Science, Vol. 13142), Björn Þór Jónsson, Cathal Gurrin, Minh-Triet Tran, Duc-Tien Dang-Nguyen, Anita Min-Chun Hu, Huynh Thi Thanh Binh, and Benoit Huet (Eds.). Springer, 537–542. https://doi.org/10.1007/978-3-030-98355-0_51
[17]
Feng-Cheng Lin, Huu-Huy Ngo, and Chyi-Ren Dow. 2020. A cloud-based face video retrieval system with deep learning. The Journal of Supercomputing 76 (11 2020). https://doi.org/10.1007/s11227-019-03123-x
[18]
Jakub Lokoč, Gregor Kovalčík, Tomáš Souček, Jaroslav Moravec, and Přemysl Čech. 2019. VIRET: A Video Retrieval Tool for Interactive Known-Item Search. In Proceedings of the 2019 on International Conference on Multimedia Retrieval (Ottawa ON, Canada) (ICMR ’19). Association for Computing Machinery, New York, NY, USA, 177–181. https://doi.org/10.1145/3323873.3325034
[19]
Vali Ollah Maraghi and Karim Faez. 2022. Class-Incremental Learning on Video-Based Action Recognition by Distillation of Various Knowledge. Computational Intelligence and Neuroscience 2022 (2022).
[20]
Thao-Nhu Nguyen, Tu-Khiem Le, Van-Tu Ninh, Cathal Gurrin, Minh-Triet Tran, Nguyen Thanh Binh, Graham Healy, Annalina Caputo, and Sinéad Smyth. 2023. E-LifeSeeker: An Interactive Lifelog Search Engine for LSC’23. In Proceedings of the 6th Annual ACM Lifelog Search Challenge, LSC 2023, Thessaloniki, Greece, June 12-15, 2023, Cathal Gurrin, Björn Þór Jónsson, Duc-Tien Dang-Nguyen, Graham Healy, Jakub Lokoc, Liting Zhou, Luca Rossetto, Minh-Triet Tran, Wolfgang Hürst, Werner Bailer, and Klaus Schoeffmann (Eds.). ACM, 13–17. https://doi.org/10.1145/3592573.3593098
[21]
Tien-Thanh Nguyen-Dang, Xuan-Dang Thai, Gia-Huy Vuong, Van-Son Ho, Minh-Triet Tran, Van-Tu Ninh, Minh-Khoi Pham, Tu-Khiem Le, and Graham Healy. 2023. LifeInsight: An Interactive Lifelog Retrieval System with Comprehensive Spatial Insights and Query Assistance. In Proceedings of the 6th Annual ACM Lifelog Search Challenge, LSC 2023, Thessaloniki, Greece, June 12-15, 2023, Cathal Gurrin, Björn Þór Jónsson, Duc-Tien Dang-Nguyen, Graham Healy, Jakub Lokoc, Liting Zhou, Luca Rossetto, Minh-Triet Tran, Wolfgang Hürst, Werner Bailer, and Klaus Schoeffmann (Eds.). ACM, 59–64. https://doi.org/10.1145/3592573.3593106
[22]
Y. Onoe, N. Yokoya, K. Yamazawa, and H. Takemura. 1998. Visual surveillance and monitoring system using an omnidirectional video camera. In Proceedings. Fourteenth International Conference on Pattern Recognition (Cat. No.98EX170), Vol. 1. 588–592 vol.1. https://doi.org/10.1109/ICPR.1998.711211
[23]
Faisal Z. Qureshi and Demetri Terzopoulos. 2005. Surveillance camera scheduling: a virtual vision approach. Multimedia Systems 12 (2005), 269–283.
[24]
Jawad Rasheed, Akhtar Jamil, Amani Yahyaoui, and Ahmed Sheikh Abdullahi Madey. 2020. Automatic Video Indexing and Retrieval System for Turkish Videos. In 2020 28th Signal Processing and Communications Applications Conference (SIU). 1–4. https://doi.org/10.1109/SIU49456.2020.9302375
[25]
T. Will Richardson, Thomas Gardali, and Stephen H. Jenkins. 2009. Review and Meta-Analysis of Camera Effects on Avian Nest Success.
[26]
Pritam Sarkar and Ali Etemad. 2022. XKD: Cross-modal Knowledge Distillation with Domain Alignment for Video Representation Learning. ArXiv abs/2211.13929 (2022).
[27]
Minh-Triet Tran, Nhat Hoang-Xuan, Hoang-Phuc Trang-Trung, Thanh-Cong Le, Mai-Khiem Tran, Minh-Quan Le, Tu-Khiem Le, Van-Tu Ninh, and Cathal Gurrin. 2022. V-FIRST: A Flexible Interactive Retrieval System for Video at VBS 2022. In MultiMedia Modeling - 28th International Conference, MMM 2022, Phu Quoc, Vietnam, June 6-10, 2022, Proceedings, Part II(Lecture Notes in Computer Science, Vol. 13142), Björn Þór Jónsson, Cathal Gurrin, Minh-Triet Tran, Duc-Tien Dang-Nguyen, Anita Min-Chun Hu, Huynh Thi Thanh Binh, and Benoit Huet (Eds.). Springer, 562–568. https://doi.org/10.1007/978-3-030-98355-0_55
[28]
Mai-Khiem Tran, Viet-Tham Huynh, and Minh-Triet Tran. 2023. Leveraging Deep Learning and Knowledge Distillation for Enhanced Traffic Anomaly Detection in Transportation Systems. In 2023 International Conference on Multimedia Analysis and Pattern Recognition (MAPR). 1–6. https://doi.org/10.1109/MAPR59823.2023.10288989
[29]
Xiaogang Wang. 2013. Intelligent multi-camera video surveillance: A review. Pattern Recognit. Lett. 34 (2013), 3–19.
[30]
Meng-Chieh Wu, Ching-Te Chiu, and Kun-Hsuan Wu. 2019. Multi-teacher Knowledge Distillation for Compressed Video Action Recognition on Deep Neural Networks. ICASSP 2019 - 2019 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP) (2019), 2202–2206.
[31]
Yu Yao, Mingze Xu, Yuchen Wang, David J. Crandall, and Ella M. Atkins. 2019. Unsupervised Traffic Accident Detection in First-Person Videos. 2019 IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS) (2019), 273–280.
[32]
Yuan Yuan, Dong Wang, and Qi Wang. 2017. Anomaly Detection in Traffic Scenes via Spatial-Aware Motion Reconstruction. IEEE Transactions on Intelligent Transportation Systems 18 (2017), 1198–1209.
[33]
Tao Zhao, Junwei Han, Le Yang, Binglu Wang, and Dingwen Zhang. 2021. SODA: Weakly Supervised Temporal Action Localization Based on Astute Background Response and Self-Distillation Learning. International Journal of Computer Vision 129 (2021), 2474 – 2498.
Index Terms
- Anomaly Event Retrieval System from TV News and Surveillance Cameras
Recommendations
Detection of user-defined, semantically high-level, composite events, and retrieval of event queries
Detecting events of interest from video sequences, and searching and retrieving events from video databases are important and challenging problems. Event of interest is a very general term, since events of interest can vary significantly among different ...
Anomaly Event Detection for Sensor Networks on Apriori Algorithms and Subjective Logic
ICSCA '19: Proceedings of the 2019 8th International Conference on Software and Computer ApplicationsAiming at the challenges that anomaly events are difficult to be characterized and heterogeneous nodes can not cooperate directly in sensor network, a novel anomaly detection method for sensor networks is proposed in this paper. We use Apriori algorithm ...
An Anomaly Event Detection Method Based on GNN Algorithm for Multi-data Sources
BSCI '21: Proceedings of the 3rd ACM International Symposium on Blockchain and Secure Critical InfrastructureAnomaly event detection is crucial for critical infrastructure security(transportation system, social-ecological sector, insurance service, government sector etc.) due to its ability to reveal and address the potential cyber-threats in advance by ...
Comments
Information & Contributors
Information
Published In
December 2023
1058 pages
ISBN:9798400708916
DOI:10.1145/3628797
Copyright © 2023 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 the author(s) 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: 07 December 2023
Check for updates
Author Tags
Qualifiers
- Research-article
- Research
- Refereed limited
Funding Sources
- PhD Scholarship Programme of Vingroup Innovation Foundation
- University of Science, VNUHCM
Conference
SOICT 2023
SOICT 2023: The 12th International Symposium on Information and Communication Technology
December 7 - 8, 2023
Ho Chi Minh, Vietnam
Acceptance Rates
Overall Acceptance Rate 147 of 318 submissions, 46%
Contributors
Other Metrics
Bibliometrics & Citations
Bibliometrics
Article Metrics
- 0Total Citations
- 22Total Downloads
- Downloads (Last 12 months)14
- Downloads (Last 6 weeks)1
Reflects downloads up to 01 Mar 2025
Other Metrics
Citations
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