Efficient Search and Localization of Human Actions in Video Databases

Shao, Ling, Jones, Simon and Li, Xuelong (2014) Efficient Search and Localization of Human Actions in Video Databases. IEEE Transactions on Circuits and Systems for Video Technology, 24 (3). pp. 504-512. ISSN 1051-8215

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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.

Item Type: Article
Uncontrolled Keywords: human actions,relevance feedback,spatiotemporal localization,video retrieval
Faculty \ School: Faculty of Science > School of Computing Sciences
Related URLs:
Depositing User: Pure Connector
Date Deposited: 07 Feb 2017 02:39
Last Modified: 22 Apr 2020 02:40
URI: https://ueaeprints.uea.ac.uk/id/eprint/62323
DOI: 10.1109/TCSVT.2013.2276700

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