Real-time video event detection in crowded scenes using MPEG derived features : a multiple instance learning approach

Xu, Jingxin, Denman, Simon, Reddy, Vikas, Fookes, Clinton B., & Sridharan, Sridha (2014) Real-time video event detection in crowded scenes using MPEG derived features : a multiple instance learning approach. Pattern Recognition Letters, 44, pp. 113-125.

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This paper presents an investigation into event detection in crowded scenes, where the event of interest co-occurs with other activities and only binary labels at the clip level are available. The proposed approach incorporates a fast feature descriptor from the MPEG domain, and a novel multiple instance learning (MIL) algorithm using sparse approximation and random sensing. MPEG motion vectors are used to build particle trajectories that represent the motion of objects in uniform video clips, and the MPEG DCT coefficients are used to compute a foreground map to remove background particles. Trajectories are transformed into the Fourier domain, and the Fourier representations are quantized into visual words using the K-Means algorithm. The proposed MIL algorithm models the scene as a linear combination of independent events, where each event is a distribution of visual words. Experimental results show that the proposed approaches achieve promising results for event detection compared to the state-of-the-art.

Impact and interest:

3 citations in Scopus
2 citations in Web of Science®
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11 since deposited on 12 Dec 2013
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ID Code: 65301
Item Type: Journal Article
Refereed: Yes
Additional URLs:
DOI: 10.1016/j.patrec.2013.11.019
ISSN: 0167-8655
Subjects: Australian and New Zealand Standard Research Classification > INFORMATION AND COMPUTING SCIENCES (080000) > ARTIFICIAL INTELLIGENCE AND IMAGE PROCESSING (080100) > Computer Vision (080104)
Divisions: Past > QUT Faculties & Divisions > Faculty of Science and Technology
Copyright Owner: Copyright 2013 Elsevier
Copyright Statement: This is the author’s version of a work that was accepted for publication in Pattern Recognition Letters. Changes resulting from the publishing process, such as peer review, editing, corrections, structural formatting, and other quality control mechanisms may not be reflected in this document. Changes may have been made to this work since it was submitted for publication. A definitive version was subsequently published in Pattern Recognition Letters, [VOL 44, (2014)] DOI: 10.1016/j.patrec.2013.11.019
Deposited On: 12 Dec 2013 00:21
Last Modified: 20 Jul 2017 03:01

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