Human Shoulder Height Measurement based on Kinect V2 Skeleton under Surveillance Conditions

Interest in the security of individuals has increased in recent years. This trend led to much wider deployment of surveillance cameras both indoor and outdoor. Consequently, more approaches are focusing on improving the mediocre performance of classical biometrics, such as face or iris, under uncontrolled conditions such as illumination and facing directions. To address the problems, soft biometrics, including hair color, ethnicity or height, were proposed. These type of biometrics can be obtained at a distance without subject cooperation, making them ideal for surveillance applications. Among many soft biometric traits, the height trait is one of the most visible and distinctive traits in unconstrained environment. Therefore, an approach to measure human shoulder height based on Kinect V2 skeleton is proposed to assist in human identification. By analyzing the data provided by Kinect V2 skeleton, the preliminary shoulder height can be estimated. Then, a compensation is applied to make the estimated shoulder height match with human true shoulder height. The results show that the accuracy error in static pose is 16mm and the mean absolute error and fluctuation deviation in dynamic pose are 16mm and 19mm, respectively. In addition, the test results in multi-person scenarios show that the proposed approach is more resistant to occlusions, which indicates more applicable in real scenes.