skip to main content
10.1145/3459637.3481980acmconferencesArticle/Chapter ViewAbstractPublication PagescikmConference Proceedingsconference-collections
short-paper

Videolytics: System for Data Analytics of Video Streams

Published:30 October 2021Publication History

ABSTRACT

We present Videolytics, a web-based system for advanced analytics over recorded video streams. Video cameras have become widely used for indoor and outdoor surveillance. Covering even more public space in cities, the cameras serve various purposes ranging from security to traffic monitoring, urban life, and marketing. The goal is to obtain effective and efficient models to process the video data automatically and produce the desired features for data analytics. Videolytics combines the best of deep learning and hand-designed analytical models to create a solution applicable in real-life situations. The architecture of the Videolytics framework is centered around a database of video features and detected objects, where new higher-level objects result from fusion of (lower-level) objects and features already stored in the database. The system provides a number of visualization options, an SQL-based analytics module as well as a real-time surveillance mode.

References

  1. AllGoVision Video Analytics. 2021 a. https://www.govision.com/allgovision-analytics.php.Google ScholarGoogle Scholar
  2. PureActiv Video Analytics. 2021 b. https://www.puretechsystems.com/request-a-live-demo.Google ScholarGoogle Scholar
  3. J Arraiza, N Aginako, S Kioumourtzis, G Leventakis, G Stavropoulos, D Tzovaras, N Zotos, A Sideris, E Charalambous, and N Koutras. 2015. Fighting volume crime: an intelligent, scalable, and low cost approach. Journal of Polish Safety and Reliability Association, Vol. 6 (2015). http://p-react.eu/Google ScholarGoogle Scholar
  4. Axxonsoft. 2021. https://axxonsoft.com/intelligence/artificial_intelligence/.Google ScholarGoogle Scholar
  5. Johan Bissmark and Oscar Wärnling. 2017. The Sparse Data Problem Within Classification Algorithms: The Effect of Sparse Data on the Naïve Bayes Algorithm.Google ScholarGoogle Scholar
  6. CrowdANALYTIX. 2021. https://www.crowdanalytix.com/.Google ScholarGoogle Scholar
  7. Marek Dobranský. 2019. Object Detection for video surveillance using SSD approach. (2019). http://hdl.handle.net/20.500.11956/107024Google ScholarGoogle Scholar
  8. Marek Dobranský and Tomá vs Skopal. 2021. On Fusion of Learned and Designed Features for Video Data Analytics. In MultiMedia Modeling - 27th International Conference, MMM 2021, Prague, Czech Republic, June 22-24, 2021, Proceedings, Part II (Lecture Notes in Computer Science, Vol. 12573). Springer, 268--280. https://doi.org/10.1007/978-3-030-67835-7_23Google ScholarGoogle Scholar
  9. Daniel Kang, John Emmons, Firas Abuzaid, Peter Bailis, and Matei Zaharia. 2017. NoScope: Optimizing Neural Network Queries over Video at Scale. Proc. VLDB Endow., Vol. 10, 11 (Aug. 2017), 1586--1597. https://doi.org/10.14778/3137628.3137664 Google ScholarGoogle ScholarDigital LibraryDigital Library
  10. X. Li, C. X. Ling and H. Wang. 2015. The Convergence Behavior of Naive Bayes on Large Sparse Datasets. In 2015 IEEE International Conference on Data Mining. 853--858. https://doi.org/10.1109/ICDM.2015.53 Google ScholarGoogle ScholarDigital LibraryDigital Library
  11. Suzanne Little, Iveel Jargalsaikhan, Kathy Clawson, Hao Li, Marcos Nieto, Cem Direkoglu, Noel E O'Connor, Alan F Smeaton, Aitor Rodriguez, Pedro Sanchez, et al. 2012. SAVASA Project@ TRECVid 2012: Interactive surveillance event detection. (2012).Google ScholarGoogle Scholar
  12. Wei Liu, Dragomir Anguelov, Dumitru Erhan, Christian Szegedy, Scott Reed, Cheng-Yang Fu, and Alexander C Berg. 2016. Ssd: Single shot multibox detector. In European conference on computer vision. Springer, 21--37.Google ScholarGoogle ScholarCross RefCross Ref
  13. David Schreiber, Martin Boyer, Peter Gemeiner, and Andreas Opitz. 2019. Generic Object Detection and Tracking for Accelerating Video Analysis within VICTORIA. In 2019 International Conference on Speech Technology and Human-Computer Dialogue (SpeD). 1--6. https://www.victoria-project.eu/Google ScholarGoogle Scholar
  14. Senstar. 2021. https://senstar.com/products/video-analytics/.Google ScholarGoogle Scholar
  15. Sentinel. 2021. https://www.sentinelcv.com/.Google ScholarGoogle Scholar
  16. PureTech Systems. 2021. PRODUCTS: VIDEO ANALYTICS., https://www.puretechsystems.com/video-analytics.html.Google ScholarGoogle Scholar
  17. TimeRethink. 2021. https://timerethink.com/.Google ScholarGoogle Scholar
  18. Bosch Security video analytics. 2021. https://resources-boschsecurity-cdn.azureedge.net/public/documents/DS_IVA_7.10_Data_sheet_enUS_69630079883.pdf.Google ScholarGoogle Scholar
  19. Videolytics. 2021. http://videolytics.ms.mff.cuni.cz.Google ScholarGoogle Scholar
  20. Videonetics. 2021. https://www.videonetics.com/products/ai-enabled-video-analytics/.Google ScholarGoogle Scholar

Index Terms

  1. Videolytics: System for Data Analytics of Video Streams

    Recommendations

    Comments

    Login options

    Check if you have access through your login credentials or your institution to get full access on this article.

    Sign in
    • Published in

      cover image ACM Conferences
      CIKM '21: Proceedings of the 30th ACM International Conference on Information & Knowledge Management
      October 2021
      4966 pages
      ISBN:9781450384469
      DOI:10.1145/3459637

      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: 30 October 2021

      Permissions

      Request permissions about this article.

      Request Permissions

      Check for updates

      Qualifiers

      • short-paper

      Acceptance Rates

      Overall Acceptance Rate1,861of8,427submissions,22%

      Upcoming Conference

    PDF Format

    View or Download as a PDF file.

    PDF

    eReader

    View online with eReader.

    eReader