Visual based fall detection with reduced complexity horprasert segmentation using superpixel

Abstract

Apart from wearable sensors and floor sensors, remote fall detection systems can be realized using camera sensors and computer visions methods and this visual based system is accurate, non-intrusive and capable to perform post fall event analysis with the recorded video. To implement visual based fall detection, the foreground segmentation process is crucial in order to provide the right foreground region with useful features for fall detection and analysis. However, in an indoor environment, change of global illumination, shadow occurrence and colour camouflage tend to occur and affect the performance of foreground extraction. Existing techniques attempted to overcome these issues are compromised with higher computational complexity and longer processing speed. Thus, an approach of using Horprasert algorithm incorporating superpixel clustering is proposed to perform background modeling and background segmentation. The foreground extracted by the proposed method is then tested against two different fall detection methods, using bounding box and motion quantification with approximated ellipse. The result has shown reduction in complexity and improvement in processing speed, without much disparity compared to the original Horprasert segmentation.