Toward efficient and intelligent video analytics with visual privacy protection for large-scale surveillance
Journal of Supercomputing, cilt.77, sa.12, ss.14374-14404, 2021 (SCI-Expanded, Scopus)
- Yayın Türü: Makale / Tam Makale
- Cilt numarası: 77 Sayı: 12
- Basım Tarihi: 2021
- Doi Numarası: 10.1007/s11227-021-03865-7
- Dergi Adı: Journal of Supercomputing
- Derginin Tarandığı İndeksler: Science Citation Index Expanded (SCI-EXPANDED), Scopus, Academic Search Premier, Applied Science & Technology Source, Compendex, Computer & Applied Sciences, INSPEC, zbMATH
- Sayfa Sayıları: ss.14374-14404
- Anahtar Kelimeler: Intelligent video analytics, Large-scale surveillance, Visual privacy, Human activity analysis, Big data, Apache spark
- Ankara Yıldırım Beyazıt Üniversitesi Adresli: Hayır
Özet
© 2021, The Author(s), under exclusive licence to Springer Science+Business Media, LLC, part of Springer Nature.Nowadays, the explosion of CCTV cameras has resulted in an increasing demand for distributed solutions to efficiently process the vast volume of video data. Otherwise, the use of surveillance when people are being watched remotely and recorded continuously has raised a significant threat to visual privacy. Using existing systems cannot prevent any party from exploiting unwanted personal data of others. In this paper, we develop an intelligent surveillance system with integrated privacy protection, where it is built on the top of big data tools, i.e., Kafka and Spark Streaming. To protect individual privacy, we propose a privacy-preserving solution based on effective face recognition and tracking mechanisms. Particularly, we associate body pose with face to reduce privacy leaks across video frames. The body pose is also exploited to infer person-centric information like human activities. Extensive experiments conducted on benchmark datasets further demonstrate the efficiency of our system for various vision tasks.