Robust Real-Time Periodic Motion Detection, Analysis, and Applications

  • Authors:
  • Ross Cutler;Larry S. Davis

  • Affiliations:
  • Univ. of Maryland, College Park;Univ. of Maryland, College Park

  • Venue:
  • IEEE Transactions on Pattern Analysis and Machine Intelligence
  • Year:
  • 2000

Quantified Score

Hi-index 0.14

Visualization

Abstract

We describe new techniques to detect and analyze periodic motion as seen from both a static and a moving camera. By tracking objects of interest, we compute an object's self-similarity as it evolves in time. For periodic motion, the self-similarity measure is also periodic and we apply Time-Frequency analysis to detect and characterize the periodic motion. The periodicity is also analyzed robustly using the 2D lattice structures inherent in similarity matrices. A real-time system has been implemented to track and classify objects using periodicity. Examples of object classification (people, running dogs, vehicles), person counting, and nonstationary periodicity are provided.