Action recognition based on multi-channel convolutional aggregation
In order to solve the problem of different action sequences in video data with varying lengths and fixed input video frame sequences,which leads to the neglect of different temporal features,an action recognition method based on a multi convolutional network aggregation deep learning model is proposed.The network uses images of different sequence lengths and modalities as input sources,consisting of three branches.Multiple branches are used to capture feature information at different scales layer by layer.At the end of the network,the features are aggregated and the rec-ognition results are classified using a softmax classifier.The experimental results show that the accuracy of this model on the UCF101 dataset reaches 88.36％,which is better than the compara-tive experimental model and effectively improves the recognition accuracy,with a certain degree of competitiveness.
deep learningaction recognitionfeature aggregationresidual structuresequence charac-teristics
万方数据
维普
