摘要
提出了一种基于Kinect和金字塔特征的行为识别算法。在算法中,Kinect不仅能够获得RGB信息,还能获得与RGB信息对应的深度信息;而金字塔特征不仅描述了人体行为的全局形状和局部细节信息,而且还描述了人体行为的空间信息。通过不同核函数的支持向量机(SVM)分类器在具有挑战性的DHA数据集的试验结果表明,金字塔特征在RGB和深度图上都能获得令人满意的性能,且当深度特征和RGB特征融合时,其性能获得了进一步的提高,识别率达到96.2%,远高于一些具有代表性的行为描述子。
Although many different behavior recognition algorithms have been prop osed,most of them are applied into RGB video sequences.What is worse,their performance is not satisfactory .Thus,in this paper a behavior recognition algorithm based on Kinect and pyramid features is proposed,in which Kinect is able to obtain RGB information,and the corresponding depth information is very help ful for our task.What is more, pyramid features not only describe the global shape and the local details of hu man behavior,but also depict the spatial information of human behavior.The combination of multi-kernel support v ector machine （SVM） classifier in challenging DHA dataset experimental results show that the performance of pyramid features on bo th RGB and the depth channels is satisfactory,and when these features are fu sed,its recognition rate is further improved to 96.2%,whose performance is much better than that of the state of the art behavior descriptors.
出处
《光电子．激光》
EI
CAS
CSCD
北大核心
2014年第2期357-363,共7页
Journal of Optoelectronics·Laser
基金
国家自然科学基金(61202168
61201234)资助项目
