低层特征与高层语义融合的人体行为识别方法

Human Activity Recognition Method Based on Low-lever Feature and High-lever Semantic

针对现有跌倒检测中跌倒样本采集困难,跌倒行为样本规模较少导致的识别率较差的问题,提出一种基于低层特征与高层语义的人体行为识别方法.该方法引入语义属性特征以便在某些行为样本较少的情况下能够共享行为之间的低层特征信息,通过构建属性-行为矩阵,利用低层特征信息训练语义属性检测器,得到语义属性特征,对属性特征与低层特征分别进行预分类,融合两种特征的预分类结果得到最终判决的人体行为类别.实验结果表明,与过采样算法、欠采样算法和最小二乘支持向量机相比,本文所提方法获得了更好的分类结果.

In order to solve the poor recognition performance of current fall detection caused by fall samples collected with difficulty and small size of fall samples in database,a human activity recognition method based on the low-level features and high-lever semantic was proposed.The semantic attributes between the low-features and the categories were introduced,which could be used to share the feature information of activities in the case of the activity had few samples.The attribute detectors were trained by the attribute-activity matrix and the low-features,and thus the attribute features were obtained.Then,the random forest algorithm was applied to recognize the attribute features and low-level features.Finally,the final result obtained by fusing the two modal pre-recognition results.The experimental results show that the proposed method has better classification performance compared with other methods.