Spatiotemporal motion analysis for the detection and classification of moving targets

Duan Yu Chen, Kevin Cannons, Hsiao Rong Tyan, Sheng Wen Shih, Hong Yuan Mark Liao

Research output: Contribution to journalArticlepeer-review

18 Citations (Scopus)


This paper presents a video surveillance system in the environment of a stationary camera that can extract moving targets from a video stream in real time and classify them into predefined categories according to their spatiotemporal properties. Targets are detected by computing the pixel-wise difference between consecutive frames, and then classified with a temporally boosted classifier and spatiotemporal-oriented energy analysis. We demonstrate that the proposed classifier can successfully recognize five types of objects: a person, a bicycle, a motorcycle, a vehicle, and a person with an umbrella. In addition, we process targets that do not match any of the AdaBoost-based classifier's categories by using a secondary classification module that categorizes such targets as crowds of individuals or non-crowds. We show that the above classification task can be performed effectively by analyzing a target's spatiotemporal-oriented energies, which provide a rich description of the target's spatial and dynamic features. Our experiment results demonstrate that the proposed system is extremely effective in recognizing all predefined object classes.

Original languageEnglish
Article number4671051
Pages (from-to)1578-1591
Number of pages14
JournalIEEE Transactions on Multimedia
Issue number8
Publication statusPublished - Dec 2008


  • Object classification
  • Spatiotemporal analysis
  • Video surveillance

ASJC Scopus subject areas

  • Signal Processing
  • Media Technology
  • Computer Science Applications
  • Electrical and Electronic Engineering


Dive into the research topics of 'Spatiotemporal motion analysis for the detection and classification of moving targets'. Together they form a unique fingerprint.

Cite this