Lost world: Looking for anomalous tracks in long-term surveillance videos

John See, Suyin Tan

Research output: Chapter in Book/Report/Conference proceedingConference contribution

5 Citations (Scopus)

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.

Original languageEnglish
Title of host publicationIVCNZ '14
Subtitle of host publicationProceedings of the 29th International Conference on Image and Vision Computing New Zealand
PublisherAssociation for Computing Machinery
Pages224-229
Number of pages6
ISBN (Electronic)9781450331845
DOIs
Publication statusPublished - 19 Nov 2014
Event29th International Conference on Image and Vision Computing New Zealand 2014 - Hamilton, New Zealand
Duration: 19 Nov 201421 Nov 2014

Conference

Conference29th International Conference on Image and Vision Computing New Zealand 2014
Abbreviated titleIVCNZ 2014
CountryNew Zealand
CityHamilton
Period19/11/1421/11/14

Keywords

  • Anomaly mining
  • Long-term video surveillance
  • Track clustering
  • Video data mining

ASJC Scopus subject areas

  • Software
  • Human-Computer Interaction
  • Computer Vision and Pattern Recognition
  • Computer Networks and Communications

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