UAV aerial video action recognition based on spatial grouping GCN
Human action recognition is a key technology for understanding pedestrian intentions from video captured by unmanned aerial vehicles(UAV).However,UAV platforms have limited computing power,and existing action recognition methods are inefficient.A lightweight spatial grouping attention graph convolutional network(SGA-GCN)was proposed to reduce network depth to improve the effi-ciency and ensure the accuracy of action recognition.In order to capture body parts that represent global motion,spatial grouping attention was introduced to enhance local features with high similarity to glob-al features.Moreover,since it was impossible to effectively distinguish actions with similar motion tra-jectories solely based on joint and skeletal features,a high-order feature encoding of skeletal angles was constructed to capture changes in angles between limb joints that better reflected subtle motion dif-ferences and improved feature representation capabilities.Finally,to address the low frame rate issue in UAV aerial video,a linear interpolation scheme based on inter-frame differences was proposed to in-crease sample information quantity.Experimental results demonstrate that compared to the existing state-of-the-art(SOTA)methods,the proposed approach achieves better performance in terms of recog-nition rate,parameter quantity,training time and execution time on the UAV-Human dataset.
万方数据
维普
