Video-based fall detection in the home using principal component analysis

This paper presents the design and real-time implementation of a fall-detection system, aiming at detecting fall incidents in unobserved home situations. The setup employs two fixed, uncalibrated, perpendicular cameras. The foreground region is extracted from both cameras and for each object, principal component analysis is employed to determine the direction of the main axis of the body and the ratio of the variances in x and y direction. A Gaussian multi-frame classifier helps to recognize fall events using the above two features. The robustness of the system is increased by a head-tracking module, that can reject false positives. We evaluate both performance and efficiency of the system for a variety of scenes: unoccluded situations, cases where the person carries objects and occluded situations. Experiments show that our algorithm can operate at real-time speed with more than 85% fall-detection rate.