基于块稀疏贝叶斯学习的人体运动模式识别

在人体运动模式识别中, 传统稀疏表示分类算法未考虑待测试样本相应稀疏系数向量内在块结构相关性信息,影响了算法识别性能。为此,提出一种基于块稀疏模型的人体运动模式识别方法。该方法充分利用人体运动模式内在块稀疏结构,将人体运动模式识别问题转化为稀疏表示问题,采用块稀疏贝叶斯学习算法,求解基于样本训练集优化稀疏表示待测样本的稀疏系数, 并根据稀疏系数重构残差判定待识别动作类别,能有效提高人体运动模式识别率。选用包含多类别人体动作行为模式的USC-HAD数据库对所提算法性能进行了验证。实验结果表明,所提算法能够有效捕获不同运动模式内在差异信息,平均动作识别率达到97.86%,比传统动作识别方法平均提高近5%,有效提高了动作识别准确率。

Human activity pattern recognition based on block sparse Bayesian learning

It is difficult for the traditional Sparse Representation Classification (SRC) algorithm to enhance the performance of human activity recognition because of ignoring the correlation structure information hidden in sparse coefficient vectors of the test sample. To address this problem, a block sparse model-based human activity recognition approach was proposed. The human activity recognition problem was considered as a sparse representation-based classification problem on the basis of the inherent sparse block structure in human activity pattern. The block sparse Bayesian learning algorithm was used to solve the optimal sparse representation coefficients of a test sample for a linear combination of the training samples from the same class, and then the reconstruction residual of sparse coefficients was defined to determine the class of the test sample, which effectively improved the recognition rate of human activity pattern. The USC-HAD database containing different styles of human daily activity was selected to evaluate the effectiveness of the proposed approach. The experimental results show that the activity recognition rate of the proposed approach reaches 97.86%, which is increasd by 5% compared to the traditional human activity methods. These results demonstrate that the proposed method can effectively capture the discriminative information of the different activity pattern, and significantly improve the accuracy of human activity recognition.