Abstract
This paper proposes a novel approach that detects and tracks carried objects by modelling the person-carried object relationship that is characteristic of the carry event. In order to detect a generic class of carried objects, we propose the use of geometric shape models, instead of using pre-trained object class models or solely relying on protrusions. In order to track the carried objects, we propose a novel optimization procedure that combines spatio-temporal consistency characteristic of the carry event, with conventional properties such as appearance and motion smoothness respectively. The proposed approach substantially outperforms a state-of-the-art approach on two challenging datasets PETS2006 and MINDSEYE2012.
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Tavanai, A., Sridhar, M., Gu, F., Cohn, A.G., Hogg, D.C. (2013). Carried Object Detection and Tracking Using Geometric Shape Models and Spatio-temporal Consistency. In: Chen, M., Leibe, B., Neumann, B. (eds) Computer Vision Systems. ICVS 2013. Lecture Notes in Computer Science, vol 7963. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-642-39402-7_23
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DOI: https://doi.org/10.1007/978-3-642-39402-7_23
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