scholarly journals Indian Classical Dance Action Identification and Classification with Convolutional Neural Networks

2018 ◽  
Vol 2018 ◽  
pp. 1-10 ◽  
Author(s):  
P. V. V. Kishore ◽  
K. V. V. Kumar ◽  
E. Kiran Kumar ◽  
A. S. C. S. Sastry ◽  
M. Teja Kiran ◽  
...  
Keyword(s):  
Neural Networks ◽  
Convolutional Neural Networks ◽  
Recognition Rate ◽  
Human Action Recognition ◽  
Human Action ◽  
Action Identification ◽  
Classical Dance ◽  
Human Movements ◽  
Indian Classical Dance

Extracting and recognizing complex human movements from unconstrained online/offline video sequence is a challenging task in computer vision. This paper proposes the classification of Indian classical dance actions using a powerful artificial intelligence tool: convolutional neural networks (CNN). In this work, human action recognition on Indian classical dance videos is performed on recordings from both offline (controlled recording) and online (live performances, YouTube) data. The offline data is created with ten different subjects performing 200 familiar dance mudras/poses from different Indian classical dance forms under various background environments. The online dance data is collected from YouTube for ten different subjects. Each dance pose is occupied for 60 frames or images in a video in both the cases. CNN training is performed with 8 different sample sizes, each consisting of multiple sets of subjects. The remaining 2 samples are used for testing the trained CNN. Different CNN architectures were designed and tested with our data to obtain a better accuracy in recognition. We achieved a 93.33% recognition rate compared to other classifier models reported on the same dataset.

scholarly journals Hierarchical dynamic depth projected difference images–based action recognition in videos with convolutional neural networks

2019 ◽  
Vol 16 (1) ◽  
pp. 172988141882509 ◽  
Author(s):  
Hanbo Wu ◽  
Xin Ma ◽  
Yibin Li
Keyword(s):  
Neural Networks ◽  
Convolutional Neural Networks ◽  
Action Recognition ◽  
Human Action Recognition ◽  
Human Action ◽  
Temporal Information ◽  
Superior Performance ◽  
Video Sequences ◽  
Depth Video ◽  
Difference Images

Temporal information plays a significant role in video-based human action recognition. How to effectively extract the spatial–temporal characteristics of actions in videos has always been a challenging problem. Most existing methods acquire spatial and temporal cues in videos individually. In this article, we propose a new effective representation for depth video sequences, called hierarchical dynamic depth projected difference images that can aggregate the action spatial and temporal information simultaneously at different temporal scales. We firstly project depth video sequences onto three orthogonal Cartesian views to capture the 3D shape and motion information of human actions. Hierarchical dynamic depth projected difference images are constructed with the rank pooling in each projected view to hierarchically encode the spatial–temporal motion dynamics in depth videos. Convolutional neural networks can automatically learn discriminative features from images and have been extended to video classification because of their superior performance. To verify the effectiveness of hierarchical dynamic depth projected difference images representation, we construct a hierarchical dynamic depth projected difference images–based action recognition framework where hierarchical dynamic depth projected difference images in three views are fed into three identical pretrained convolutional neural networks independently for finely retuning. We design three classification schemes in the framework and different schemes utilize different convolutional neural network layers to compare their effects on action recognition. Three views are combined to describe the actions more comprehensively in each classification scheme. The proposed framework is evaluated on three challenging public human action data sets. Experiments indicate that our method has better performance and can provide discriminative spatial–temporal information for human action recognition in depth videos.

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