A simple vision-based fall detection technique for indoor video surveillance

Falls are one of the major health hazards among the aging population aged 65 and above, which could potentially result in a significant hindrance to their independent living. With the advances in medical science in the last few decades, the aging population increases every year, and thus, fall detection system at home is increasingly important. This paper presents a new vision-based fall detection technique that is based on human shape variation where only three points are used to represent a person instead of the conventional ellipse or bounding box. Falls are detected by analyzing the shape change of the human silhouette through the features extracted from the three points. Experiment results show that in comparison with the conventional ellipse and bounding box techniques, the proposed three point–based technique increases the fall detection rate without increasing the computational complexity.