Efficient Search and Localization of Human Actions in Video Databases

Ling Shao, Simon Jones, Xuelong Li

Research output: Contribution to journalArticle

55 Citations (Scopus)
2 Downloads (Pure)

Abstract

As digital video databases grow, so grows the problem of effectively navigating through them. In this paper we propose a novel content-based video retrieval approach to searching such video databases, specifically those involving human actions, incorporating spatio-temporal localization. We outline a novel, highly efficient localization model that first performs temporal localization based on histograms of evenly spaced time-slices, then spatial localization based on histograms of a 2-D spatial grid. We further argue that our retrieval model, based on the aforementioned localization, followed by relevance ranking, results in a highly discriminative system, while remaining an order of magnitude faster than the current state-of-the-art method. We also show how relevance feedback can be applied to our localization and ranking algorithms. As a result, the presented system is more directly applicable to real-world problems than any prior content-based video retrieval system.
Original languageEnglish
Pages (from-to)504-512
Number of pages9
JournalIEEE Transactions on Circuits and Systems for Video Technology
Volume24
Issue number3
Early online date6 Aug 2013
DOIs
Publication statusPublished - 1 Mar 2014

Keywords

  • Human actions
  • relevance feedback
  • spatiotemporal localization
  • video retrieval

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