Abstract
We present a method to detect people waving using video streams from a fixed camera system. Waving is a natural means of calling for attention and can be used by citizens to signal emergency events or abnormal situations in future automated surveillance systems. Our method is based on training a supervised classifier using a temporal boosting method based on optical flow-derived features. The base algorithm shows a low false positive rate and if further improves through the definition of a minimum time for the duration of the waving event. The classifier generalizes well to scenarios very different from where it was trained. We show that a system trained indoors with high resolution and frontal postures can operate successfully, in real-time, in an outdoor scenario with large scale differences and arbitrary postures.
Research partly funded by the FCT Programa Operacional Sociedade de Informação(POSI) in the frame of QCA III, and EU Project URUS (IST-045062).
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Moreno, P., Bernardino, A., Santos-Victor, J. (2009). Waving Detection Using the Local Temporal Consistency of Flow-Based Features for Real-Time Applications. In: Kamel, M., Campilho, A. (eds) Image Analysis and Recognition. ICIAR 2009. Lecture Notes in Computer Science, vol 5627. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-642-02611-9_87
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DOI: https://doi.org/10.1007/978-3-642-02611-9_87
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