This paper proposes a human body orientation estimation method using the Kinect camera depth data. The input of our system consists of three one-dimensional distance-based signals which reflect the body’s surface contours of the human upper body portion, i.e., the upper chest, upper abdomen, and lower abdomen. Such signals are then normalized using their distances to achieve the same amount of the lower parts. All normalized signals are concatenated to provide a mix of contour features. We used Support Vector Regression (SVR) to classify the feature and Kalman Filter to estimate the continuous orientations instead of using discrete orientations. We also extend our work by adding human motion direction to the robust estimate of human body orientation when walking. We conducted two evaluation schemes, i.e., body orientation at static position and body orientation when moving. The experimental results show that our system achieves impressive results by achieving mean average of angle error (MAAE) of \(0.097^{\circ }\) and \(5.82^{\circ }\) for estimating body continuous orientation at static position and estimating body continuous orientation when moving, respectively. Therefore, it is very promising to be applied in real implementations.

本文提出了一种使用 Kinect 相机深度数据的人体方向估计方法。我们系统的输入由三个基于距离的一维信号组成，这些信号反映了人体上半身部分的身体表面轮廓，即上胸部、上腹部和下腹部。然后使用它们的距离对这些信号进行归一化，以实现相同数量的较低部分。所有归一化的信号都被连接起来以提供轮廓特征的混合。我们使用支持向量回归 (SVR) 对特征进行分类，并使用卡尔曼滤波器来估计连续方向，而不是使用离散方向。我们还通过将人体运动方向添加到步行时人体方向的稳健估计来扩展我们的工作。我们进行了两种评估方案，即 静态时的身体方向和移动时的身体方向。实验结果表明，我们的系统通过实现平均角度误差 (MAAE)\(0.097^{\circ }\)和\(5.82^{\circ }\)分别用于估计身体在静态位置的连续方向和估计身体在移动时的连续方向。因此，在实际应用中应用是非常有希望的。