A new model for classification of human movements on videos using convolutional neural networks: MA-Net

ABSTRACT 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%.

[1]  Yurong Liu,et al.  A survey of deep neural network architectures and their applications , 2017, Neurocomputing.

[2]  Zhenpo Wang,et al.  Multi-time-scale framework for prognostic health condition of lithium battery using modified Gaussian process regression and nonlinear regression , 2020 .

[3]  Med Salim Bouhlel,et al.  A new hybrid deep learning model for human action recognition , 2020, J. King Saud Univ. Comput. Inf. Sci..

[4]  Zafer Cömert,et al.  Detection of lung cancer on chest CT images using minimum redundancy maximum relevance feature selection method with convolutional neural networks , 2020 .

[5]  Jian Sun,et al.  Deep Residual Learning for Image Recognition , 2015, 2016 IEEE Conference on Computer Vision and Pattern Recognition (CVPR).

[6]  Geoffrey E. Hinton,et al.  ImageNet classification with deep convolutional neural networks , 2012, Commun. ACM.

[7]  Muhammed Yildirim,et al.  Classification of White Blood Cells by Deep Learning Methods for Diagnosing Disease , 2019, Rev. d'Intelligence Artif..

[8]  Md. Zia Uddin,et al.  A Depth Camera-based Human Activity Recognition via Deep Learning Recurrent Neural Network for Health and Social Care Services , 2016, CENTERIS/ProjMAN/HCist.

[9]  Ying Wah Teh,et al.  Deep learning algorithms for human activity recognition using mobile and wearable sensor networks: State of the art and research challenges , 2018, Expert Syst. Appl..

[10]  Yifan Zhou,et al.  Fault diagnosis of multi-channel data by the CNN with the multilinear principal component analysis , 2021 .

[11]  Stan Sclaroff,et al.  Space-time tree ensemble for action recognition , 2015, 2015 IEEE Conference on Computer Vision and Pattern Recognition (CVPR).

[12]  Ahmet Çinar,et al.  Classification of Alzheimer's Disease MRI Images with CNN Based Hybrid Method , 2020, Ingénierie des Systèmes d Inf..

[13]  Humberto Bustince,et al.  Simulating the Behaviour of Choquet-Like (pre) Aggregation Functions for Image Resizing in the Pooling Layer of Deep Learning Networks , 2019, IFSA/NAFIPS.

[14]  Ahmet Çinar,et al.  Detection of tumors on brain MRI images using the hybrid convolutional neural network architecture. , 2020, Medical hypotheses.

[15]  Muhammed Yildirim,et al.  Classification of Pneumonia Cell Images Using Improved ResNet50 Model , 2021, Traitement du Signal.

[16]  Mubarak Shah,et al.  Action MACH a spatio-temporal Maximum Average Correlation Height filter for action recognition , 2008, 2008 IEEE Conference on Computer Vision and Pattern Recognition.

[17]  Cordelia Schmid,et al.  Dense Trajectories and Motion Boundary Descriptors for Action Recognition , 2013, International Journal of Computer Vision.

[18]  Antonios Gasteratos,et al.  On-line deep learning method for action recognition , 2014, Pattern Analysis and Applications.

[19]  Lihui Wang,et al.  Gesture recognition for human-robot collaboration: A review , 2017, International Journal of Industrial Ergonomics.

[20]  Amir Roshan Zamir,et al.  Action Recognition in Realistic Sports Videos , 2014 .

[21]  Sergey Ioffe,et al.  Rethinking the Inception Architecture for Computer Vision , 2015, 2016 IEEE Conference on Computer Vision and Pattern Recognition (CVPR).

[22]  Flavio de Barros Vidal,et al.  Human Action Recognition Based on a Two-stream Convolutional Network Classifier , 2017, 2017 16th IEEE International Conference on Machine Learning and Applications (ICMLA).

[23]  Akif Durdu,et al.  Human action recognition with bag of visual words using different machine learning methods and hyperparameter optimization , 2019, Neural Computing and Applications.

[24]  Ahmet Çinar,et al.  A Deep Learning Based Hybrid Approach for COVID-19 Disease Detections , 2020, Traitement du Signal.

[25]  Sidan Du,et al.  Image based fruit category classification by 13-layer deep convolutional neural network and data augmentation , 2019, Multimedia Tools and Applications.

[26]  Kilian Q. Weinberger,et al.  Densely Connected Convolutional Networks , 2016, 2017 IEEE Conference on Computer Vision and Pattern Recognition (CVPR).

[27]  Hemanth Kalluri,et al.  Lung Cancer Detection Based on CT Scan Images by Using Deep Transfer Learning , 2019, Traitement du Signal.

[28]  Jiaying Liu,et al.  Adaptive Batch Normalization for practical domain adaptation , 2018, Pattern Recognit..

[29]  Kalyan Chatterjee,et al.  Detection of brain abnormality by a novel Lu-Net deep neural CNN model from MR images , 2020 .