Abstract
This chapter proposes an object movement detection method in household environments. The proposed method detects “object placement” and “object removal” via images captured by environment-embedded cameras. When object movement detection is performed in household environments, there are several difficulties: the method needs to detect object movements robustly even if sizes of objects are small, the method must discriminate objects and non-objects such as humans. In this work, we propose an object movement detection method by detecting “stable changes”, which are changing from the recorded state but which change are settled. To categorize objects and non-objects via the stable changes even though non-objects make long-term changes (e.g. a person is sitting down), we employ motion history of changed regions. In addition, to classify object placement and object removal, we use multiple-layered background model, called the layered background model and edge subtraction technique. The experiment shows the system can detect objects robustly and in sufficient frame-rates.
An Erratum for this chapter can be found at http://dx.doi.org/10.1007/978-3-642-17554-1_11
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Odashima, S., Mori, T., Simosaka, M., Noguchi, H., Sato, T. (2011). Event Understanding of Human-Object Interaction: Object Movement Detection via Stable Changes. In: Zhang, J., Shao, L., Zhang, L., Jones, G.A. (eds) Intelligent Video Event Analysis and Understanding. Studies in Computational Intelligence, vol 332. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-642-17554-1_9
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DOI: https://doi.org/10.1007/978-3-642-17554-1_9
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