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Lost World: Looking for Anomalous Tracks in Long-term Surveillance Videos

Published: 19 November 2014 Publication History

Abstract

Video surveillance over a long span of time is no longer a luxury in this day and age. The abundance of video data captured over time presents a slew of new problems in computer vision. One potential challenge involves the task of finding anomalous tracks over a long period of time. In this work, we propose a new time-scale framework for mining anomalous track patterns in long-term surveillance videos. Track clustering is performed at two separate temporal levels to better represent the common modes of behaviour. A probabilistic anomaly prediction algorithm is also introduced to evaluate the abnormality of new tracks. In our preliminary work, experiments conducted on the LOST dataset offer insights into how track anomalies can be mined and classified. We hope this work will provide the impetus for further advancements in this direction.

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Cited By

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  • (2018)Lost in Time: Temporal Analytics for Long-Term Video SurveillanceComputational Science and Technology10.1007/978-981-10-8276-4_33(347-357)Online publication date: 24-Feb-2018
  • (2015)Lost and found: Identifying objects in long-term surveillance videos2015 IEEE International Conference on Signal and Image Processing Applications (ICSIPA)10.1109/ICSIPA.2015.7412171(99-104)Online publication date: Oct-2015

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IVCNZ '14: Proceedings of the 29th International Conference on Image and Vision Computing New Zealand
November 2014
298 pages
ISBN:9781450331845
DOI:10.1145/2683405
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].

In-Cooperation

  • The University of Waikato

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Association for Computing Machinery

New York, NY, United States

Publication History

Published: 19 November 2014

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Author Tags

  1. anomaly mining
  2. long-term video surveillance
  3. track clustering
  4. video data mining

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  • Research-article
  • Research
  • Refereed limited

Funding Sources

  • Multimedia University, Malaysia

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IVCNZ '14

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IVCNZ '14 Paper Acceptance Rate 55 of 74 submissions, 74%;
Overall Acceptance Rate 55 of 74 submissions, 74%

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Cited By

View all
  • (2018)Lost in Time: Temporal Analytics for Long-Term Video SurveillanceComputational Science and Technology10.1007/978-981-10-8276-4_33(347-357)Online publication date: 24-Feb-2018
  • (2015)Lost and found: Identifying objects in long-term surveillance videos2015 IEEE International Conference on Signal and Image Processing Applications (ICSIPA)10.1109/ICSIPA.2015.7412171(99-104)Online publication date: Oct-2015

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