A new model for classification of human movements on videos using convolutional neural networks: MA-Net - Yildirim, Muhammed; Cinar, Ahmet (Taylor & Francis Ltd, 2021)
Classification of human movements is essential for interpreting and describing human activities, such as environment-supported living in smart home environments, elderly nursing homes, visual tracking, object tracking, anomaly detection, medical visualisation and mimic analysis. Also, human movements recognition from videos has become one of the important issues that arise with the developing technology and the processing of big data in computers. In this paper, it is aimed to classify human movements by using a data set including different motion videos. For this aim, a new model MA-Net named by us is proposed. MA-Net have 43 layers. In order to examine MA-Net, data having150 videos and 10 classes in UCF dataset is examined. At first, the frames from videos are extracted. In study, it has been worked to take one frame in 50 frames in videos. After that, dataset is classified using well known models such as Resnet50, Alexnet, Inceptionv3, Densenet201 architectures. After, proposed new model MA-Net are classified too. The highest accuracy rate is obtained from MA-Net model as 91.34%.
Keywords: Human activity recognition; deep learning; CNN; classification