Falling down detection on zebra crossing at night by thermal imager

Abstract

Falling down detection is an important application for surveillance system. In this study, a two-stage falling down detection at night based on optical flow and motion histogram image (MHI) is proposed. Based on the thermal imager, the foreground pedestrian could be perfectly extracted. In the first stage, vertical optical flow feature is used to roughly detect the falling down event, then, in the second stage, vertical optical flow hybrid MHI feature is fed into the Naive Bayes classifier to verify the falling down event. The experimental results show that the detection rate is 98.6%, which demonstrates the effectiveness of the proposed method.