Object tracking and multimedia augmented transition network for video indexing and modeling

Shu Ching Chen, Mei-Ling Shyu, Chengcui Zhang, R. L. Kashyap

Research output: Chapter in Book/Report/Conference proceedingConference contribution

10 Scopus citations

Abstract

S.C. Chen et al. (1999) proposed a multimedia augmented transition network (ATN) model, together with its multimedia input strings, to model and structure video data. This multimedia ATN model was based on an ATN model that had been used within the artificial intelligence (AI) arena for natural-language understanding systems, and its inputs were modeled by multimedia input strings. The temporal and spatial relations of semantic objects were captured by an unsupervised video segmentation method called the SPCPE (simultaneous partitioning and class parameter estimation) algorithm, and they were modeled by the multimedia input strings. However, the segmentation method used was not able to identify objects that are overlapped together within video frames. The identification of overlapped objects is a great challenge. For this purpose, a backtrack-chain-update-split algorithm is developed in this paper that identifies the split segment (object) and uses this information in the current frame to update the previous frames in a backtrack-chain manner. The proposed split algorithm provides more accurate temporal and spatial information of the semantic objects for video indexing.

Original languageEnglish (US)
Title of host publicationProceedings - International Conference on Tools with Artificial Intelligence, ICTAI
PublisherIEEE Computer Society
Pages250-257
Number of pages8
Volume2000-January
ISBN (Print)0769509096
DOIs
StatePublished - 2000
Event12th IEEE Internationals Conference on Tools with Artificial Intelligence, ICTAI 2000 - Vancouver, Canada
Duration: Nov 13 2000Nov 15 2000

Other

Other12th IEEE Internationals Conference on Tools with Artificial Intelligence, ICTAI 2000
CountryCanada
CityVancouver
Period11/13/0011/15/00

Keywords

  • Artificial intelligence
  • Computer networks
  • Indexing
  • Information retrieval
  • Multimedia databases
  • Multimedia systems
  • Partitioning algorithms
  • Streaming media
  • Video on demand
  • Videoconference

ASJC Scopus subject areas

  • Software
  • Artificial Intelligence
  • Computer Science Applications

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  • Cite this

    Chen, S. C., Shyu, M-L., Zhang, C., & Kashyap, R. L. (2000). Object tracking and multimedia augmented transition network for video indexing and modeling. In Proceedings - International Conference on Tools with Artificial Intelligence, ICTAI (Vol. 2000-January, pp. 250-257). [889878] IEEE Computer Society. https://doi.org/10.1109/TAI.2000.889878