scholarly journals Dance Action Recognition and Pose Estimation Based on Deep Convolutional Neural Network

2021 ◽  
Vol 38 (2) ◽  
pp. 529-538
Author(s):  
Fengling Zhu ◽  
Ruichao Zhu
Keyword(s):  
Neural Network ◽  
Convolutional Neural Network ◽  
Action Recognition ◽  
Pose Estimation ◽  
Sampling Strategy ◽  
Local Features ◽  
Deep Convolutional Neural Network ◽  
Action Execution ◽  
Full Effect ◽  
Global And Local

Sports action recognition helps athletes correct their action range and standardize their poses. But it is not an easy task to recognize sports actions, due to the individual difference in action execution. Besides, the difficulty of action recognition increases with the diversity of actions and the complexity of background. The previous studies have not fully considered temporal changes, and failed to determine the exact staring point of actions. To solve the problem, this paper proposes a new method to recognize dance actions and estimate poses based on deep convolutional neural network (DCNN). Firstly, the authors presented full-effect expression of global and local features of dance actions, and derived an optimal model based on DeepPose. Next, a dance pose evaluation model was established based on time sequence segmentation network, and the sparse time sampling strategy was introduced to realize efficient and effective learning of the frame sequence of the whole video. Experimental results confirm the superiority of the full-effect expression of global and local features, and the effectiveness of the proposed model. The research results provide a reference for the application of deep learning (DL) in other scenarios of action recognition and pose estimation.

scholarly journals Vision-Based Spacecraft Pose Estimation via a Deep Convolutional Neural Network for Noncooperative Docking Operations

Aerospace ◽  
2020 ◽  
Vol 7 (9) ◽  
pp. 126 ◽  
Author(s):  
Thaweerath Phisannupawong ◽  
Patcharin Kamsing ◽  
Peerapong Torteeka ◽  
Sittiporn Channumsin ◽  
Utane Sawangwit ◽  
...  
Keyword(s):  
Neural Network ◽  
Convolutional Neural Network ◽  
Loss Function ◽  
Pose Estimation ◽  
High Performance ◽  
Degrees Of Freedom ◽  
Recognition Algorithm ◽  
Deep Convolutional Neural Network ◽  
Estimation Model ◽  
Detection And Tracking

The capture of a target spacecraft by a chaser is an on-orbit docking operation that requires an accurate, reliable, and robust object recognition algorithm. Vision-based guided spacecraft relative motion during close-proximity maneuvers has been consecutively applied using dynamic modeling as a spacecraft on-orbit service system. This research constructs a vision-based pose estimation model that performs image processing via a deep convolutional neural network. The pose estimation model was constructed by repurposing a modified pretrained GoogLeNet model with the available Unreal Engine 4 rendered dataset of the Soyuz spacecraft. In the implementation, the convolutional neural network learns from the data samples to create correlations between the images and the spacecraft’s six degrees-of-freedom parameters. The experiment has compared an exponential-based loss function and a weighted Euclidean-based loss function. Using the weighted Euclidean-based loss function, the implemented pose estimation model achieved moderately high performance with a position accuracy of 92.53 percent and an error of 1.2 m. The in-attitude prediction accuracy can reach 87.93 percent, and the errors in the three Euler angles do not exceed 7.6 degrees. This research can contribute to spacecraft detection and tracking problems. Although the finished vision-based model is specific to the environment of synthetic dataset, the model could be trained further to address actual docking operations in the future.

Sign in / Sign up

Export Citation Format

Share Document