Supporting timeliness and accuracy in distributed real-time content-based video analysis

  • Authors:

  • Viktor S. Wold Eide;Frank Eliassen;Ole-Christoffer Granmo;Olav Lysne

  • Affiliations:

  • University of Oslo, Oslo, Norway & Simula Research Laboratory, Lysaker, Norway;Simula Research Laboratory, Lysaker, Norway;University of Oslo, Oslo, Norway & Simula Research Laboratory, Lysaker, Norway & Agder University College, N-4876 Grimstad, Norway;Simula Research Laboratory, Lysaker, Norway

  • Venue:
  • MULTIMEDIA '03 Proceedings of the eleventh ACM international conference on Multimedia
  • Year:
  • 2003

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Abstract

Real-time content-based access to live video data requires content analysis applications that are able to process the video data at least as fast as the video data is made available to the application and with an acceptable error rate. Statements as this express quality of service (QoS) requirements to the application. In order to provide some level of control of the QoS provided, the video content analysis application must be scalable and resource aware so that requirements of timeliness and accuracy can be met by allocating additional processing resources.In this paper we present a general architecture of video content analysis applications including a model for specifying requirements of timeliness and accuracy. The salient features of the architecture include its combination of probabilistic knowledge-based media content analysis with QoS and distributed resource management to handle QoS requirements, and its independent scalability at multiple logical levels of distribution. We also present experimental results with an algorithm for QoS-aware selection of configurations of feature extractor and classification algorithms that can be used to balance requirements of timeliness and accuracy against available processing resources. Experiments with an implementation of a real-time motion vector based object-tracking application, demonstrate the scalability of the architecture.