Skip to Main content Skip to Navigation
Conference papers

Group Tracking and Behavior Recognition in Long Video Surveillance Sequences

Abstract : This paper makes use of recent advances in group tracking and behavior recognition to process large amounts of video surveillance data from an underground railway station and perform a statistical analysis. The most important advantages of our approach are the robustness to process long videos and the capacity to recognize several and different events at once. This analysis automatically brings forward data about the usage of the station and the various behaviors of groups in different hours of the day. This data would be very hard to obtain without an automatic group tracking and behavior recognition method. We present the results and interpretation of one month of processed data from a video surveillance camera in the Torino subway.
Complete list of metadata

Cited literature [13 references]  Display  Hide  Download

https://hal.inria.fr/hal-00879734
Contributor : Carolina Garate <>
Submitted on : Tuesday, January 14, 2014 - 10:45:06 AM
Last modification on : Thursday, March 5, 2020 - 5:34:27 PM
Long-term archiving on: : Tuesday, April 15, 2014 - 4:17:51 PM

File

VISAPP2014.pdf
Files produced by the author(s)

Identifiers

  • HAL Id : hal-00879734, version 1

Collections

Citation

Carolina Garate, Sofia Zaidenberg, Julien Badie, François Bremond. Group Tracking and Behavior Recognition in Long Video Surveillance Sequences. VISAPP - 9th International Joint Conference on Computer Vision, Imaging and Computer Graphics Theory and Applications, Jan 2014, lisbon, Portugal. ⟨hal-00879734⟩

Share

Metrics

Record views

825

Files downloads

687