Graph-based object detection and tracking in H.264/AVC bitstreams for surveillance video

Houari Sabirin, Jaeil Kim, Munchurl Kim

Research output: Chapter in Book/Report/Conference proceedingConference contributionpeer-review

6 Scopus citations

Abstract

In this paper we present a novel method to detect and track moving objects in H.264/AVC bitstreams by processing motion vector and residue information. The encoded blocks with nonzero motion vectors and residues are first detected as moving object candidates. A spatio-temporal graph in video sequences is then constructed to represent groups of blocks in each frame and their associations to the other groups of blocks in subsequent frames. Identification and refinement of ROIs for moving objects being tracked are done by graph matching and adaptive ROI-size adjustment. The experimental results show that the proposed method can correctly identify real moving objects from frame to frame and can effectively detect small-sized objects and objects with small motion vectors and residues, as well as by recognizing moving objects even under occlusion.

Original languageEnglish
Title of host publicationElectronic Proceedings of the 2011 IEEE International Conference on Multimedia and Expo, ICME 2011
DOIs
StatePublished - 2011
Event2011 12th IEEE International Conference on Multimedia and Expo, ICME 2011 - Barcelona, Spain
Duration: 11 Jul 201115 Jul 2011

Publication series

NameProceedings - IEEE International Conference on Multimedia and Expo
ISSN (Print)1945-7871
ISSN (Electronic)1945-788X

Conference

Conference2011 12th IEEE International Conference on Multimedia and Expo, ICME 2011
Country/TerritorySpain
CityBarcelona
Period11/07/1115/07/11

Keywords

  • graph theory
  • H.264/AVC
  • object detection and tracking
  • surveillance video

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