Waving detection using the local temporal consistency of flow-based features for real-time applications

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. © 2009 Springer Berlin Heidelberg.