Context-based region labeling for event detection in surveillance video

S. Javanbakhti, S. Zinger, P.H.N. With, de

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6 Citations (Scopus)
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Automatic natural scene understanding and annotating regions with semantically meaningful labels, such as road or sky, are key aspects of image and video analysis. The annotation of regions is a considered helpful for improving the object-of-interest detection because the object position in the scene is also exploited. For a reliable model of a scene and associated context information, the labeling task involves image analysis at multiple, both global and local, scene levels. In this paper, we develop a general framework for performing automatic semantic labeling of video scenes by combining the local features and spatial contextual cues. While maintaining a high accuracy, we pursue an algorithm with low computational complexity, so that it is suitable for real-time implementation in embedded video surveillance. We apply our approach to a complex surveillance use case and to three different datasets: WaterVisie [1], LabelMe [2] and our own dataset. We show that our method quantitatively and qualitatively outperforms two sate-of-the-art approaches [3][4].
Original languageEnglish
Title of host publicationProceedings 2014 International Conference on Information Science, Electronics and Electrical Engineering (ISEEE), 26-28 april 2014, Sapporo, Japan
Place of PublicationPiscataway
PublisherInstitute of Electrical and Electronics Engineers
ISBN (Print)978-1-4799-3196-5
Publication statusPublished - 2014
Eventconference; ISEEE; 2014-04-26; 2014-04-28 -
Duration: 26 Apr 201428 Apr 2014


Conferenceconference; ISEEE; 2014-04-26; 2014-04-28


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