Localization of region of interest in surveillance scene

Sk Arif Ahmed, Debi Prosad Dogra*, Samarjit Kar, Byung Gyu Kim, Paul Hill, Harish Bhaskar

*Corresponding author for this work

Research output: Contribution to journalArticle (Academic Journal)peer-review

6 Citations (Scopus)
273 Downloads (Pure)


In this paper, we present a method for autonomously detecting and extracting region(s)-of-interest (ROI) from surveillance videos using trajectory-based analysis. Our approach, localizes ROI in a stochastic manner using correlated probability density functions that model motion dynamics of multiple moving targets. The motion dynamics model is built by analyzing trajectories of multiple moving targets and associating importance to regions in the scene. The importance of each region is estimated as a function of the total time spent by multiple targets, their instantaneous velocity and direction of movement whilst passing through that region. We systematically validate our model and benchmark our technique against competing baselines through extensive experimentation using public datasets such as CAVIAR, ViSOR, and CUHK as well as a scenario-specific in-house surveillance dataset. Results obtained have demonstrated the superiority of the proposed technique against a few popular existing state-of-the-art techniques.

Original languageEnglish
Pages (from-to)13651-13680
Number of pages30
JournalMultimedia Tools and Applications
Issue number11
Early online date20 Jul 2016
Publication statusPublished - 1 Jun 2017


  • Movement analysis
  • Object tracking
  • Scene segmentation
  • Scene understanding
  • Trajectory analysis

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