A feature-based algorithm for detecting and classifying production effects

  • Authors:

  • Ramin Zabih;Justin Miller;Kevin Mai

  • Affiliations:

  • Computer Science Department, Cornell University, Ithaca, NY;Computer Science Department, Cornell University, Ithaca, NY;Computer Science Department, Cornell University, Ithaca, NY

  • Venue:
  • Multimedia Systems
  • Year:
  • 1999

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Abstract

We describe a new approach to the detection and classification of production effects in video sequences. Our method can detect and classify a variety of effects, including cuts, fades, dissolves, wipes and captions, even in sequences involving significant motion. We detect the appearance of intensity edges that are distant from edges in the previous frame. A global motion computation is used to handle camera or object motion. The algorithm we propose withstands JPEG and MPEG artifacts, even at high compression rates. Experimental evidence demonstrates that our method can detect and classify production effects that are difficult to detect with previous approaches.