Please use this identifier to cite or link to this item: https://doi.org/10.1145/1461893.1461904
Title: Categorizing Bi-object video activities using bag of segments and causality features
Authors: Zhou, Y.
Yan, S. 
Huang, T.S.
Keywords: Bag of segments
Classification
Granger Causality
Video activity
Issue Date: 2008
Source: Zhou, Y.,Yan, S.,Huang, T.S. (2008). Categorizing Bi-object video activities using bag of segments and causality features. MM'08 - Proceedings of the 2008 ACM International Conference on Multimedia, with co-located Symposium and Workshops : 55-60. ScholarBank@NUS Repository. https://doi.org/10.1145/1461893.1461904
Abstract: We address the recognition problem of video activities involving two interacting moving objects under a surveillance camera. We develop a novel video activity representation scheme - 'bag of segments'. In this scheme, the video sessions are represented as a collection of independent segments, with memberships to each pre-learned visual patterns that we call codewords. To better represent the video segments with object interaction, we design a set of new features based on the prediction filter responses and the Granger Causality Test (GCT). These features capture the inter-relationship between moving objects and are combined with conventional features such as position and velocity. We validate the proposed method for the task of video activities classification with extensive experiments on a surveillance database with 867 video sessions. © 2008 ACM.
Source Title: MM'08 - Proceedings of the 2008 ACM International Conference on Multimedia, with co-located Symposium and Workshops
URI: http://scholarbank.nus.edu.sg/handle/10635/83530
ISBN: 9781605583136
DOI: 10.1145/1461893.1461904
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