CAJ | 학술논문

基于时空特征的方法是行为识别的主流方法，已经有许多研究学者提出了多种局部时空特征。然而，不同的局部特征所反映的行为信息的侧重点并不一样。通过引入集成学习的方法，对多种特征在分类器层次上进行融合，使得多种特征能够优势互补，从而增强了特征的描述能力，为构建出高效、稳定的行为识别分类器提供了保证。经仿真实验验证，所提出的方法是鲁棒和有效的。
기우시공특정적방법시행위식별적주류방법，이경유허다연구학자제출료다충국부시공특정。연이，불동적국부특정소반영적행위신식적측중점병불일양。통과인입집성학습적방법，대다충특정재분류기층차상진행융합，사득다충특정능구우세호보，종이증강료특정적묘술능력，위구건출고효、은정적행위식별분류기제공료보증。경방진실험험증，소제출적방법시로봉화유효적。
The approach based on the local spatial-temporal features has emerged to be the mainstream method in action recognition area. And various descriptors of local spatial-temporal feature have been presented by researchers. However, different local features may reflect different emphasis of human activity. In this paper, the ensemble learning methods are introduced to perform a late fusion of multiple features so as to enhance the expressing ability of the local features. By the fusion of features, a more effective and robust action classifier can be built up. And the experimental results demonstrate the robustness and effectiveness of the proposed method.