Real time pose detection of animals using HRNet

2021 ◽  
Author(s):  
Soham Shanbhag ◽  
Dong Eui Chang
Keyword(s):  
Real Time ◽  
Pose Detection

scholarly journals Real-Time Pose Detection for Magnetic Medical Devices

2013 ◽  
Vol 49 (7) ◽  
pp. 3524-3527 ◽  
Author(s):  
Christian Di Natali ◽  
Marco Beccani ◽  
Pietro Valdastri
Keyword(s):  
Real Time ◽  
Medical Devices ◽  
Pose Detection

scholarly journals Pose detection of parallel robot based on improved RANSAC algorithm

2019 ◽  
Vol 52 (7-8) ◽  
pp. 855-868 ◽  
Author(s):  
Guo-Qin Gao ◽  
Qian Zhang ◽  
Shu Zhang
Keyword(s):  
Real Time ◽  
Parallel Robot ◽  
Feature Points ◽  
Scale Invariant ◽  
Time Performance ◽  
Invariant Feature ◽  
Pose Detection ◽  
Feature Transform ◽  
Scale Invariant Feature ◽  
Ransac Algorithm

For the factors of complex image background, unobvious end-effector characteristics and uneven illumination in the pose detection of parallel robot based on binocular vision, the detection speed, and accuracy cannot meet the requirement of the closed-loop control. So a pose detection method based on improved RANSAC algorithm is presented. First, considering that the image of parallel robot is rigid and has multiple corner points, the Harris–Scale Invariant Feature Transform algorithm is adopted to realize image prematching. The feature points are extracted by Harris and matched by Scale Invariant Feature Transform to realize good accuracy and real-time performance. Second, for the mismatching from prematching, an improved RANSAC algorithm is proposed to refine the prematching results. This improved algorithm can overcome the disadvantages of mismatching and time-consuming of the conventional RANSAC algorithm by selecting feature points in separated grids of the images and predetecting to validate provisional model. The improved RANSAC algorithm was applied to a self-developed novel 3-degrees of freedom parallel robot to verify the validity. The experiment results show that, compared with the conventional algorithm, the average matching time decreases by 63.45%, the average matching accuracy increases by 15.66%, the average deviations of pose detection in Y direction, Z direction, and roll angle [Formula: see text] decrease by 0.871 mm, 0.82 mm, and 0.704°, respectively, using improved algorithm to refine the prematching results. The real-time performance and accuracy of pose detection of parallel robot can be improved.

scholarly journals Real-Time Pose Detection and Tracking of Hundreds of Objects

2016 ◽  
Vol 26 (12) ◽  
pp. 2200-2214 ◽  
Author(s):  
Karl Pauwels ◽  
Leonardo Rubio ◽  
Eduardo Ros
Keyword(s):  
Real Time ◽  
Detection And Tracking ◽  
Pose Detection

scholarly journals Automation of Cricket Scoreboard by Recognizing Umpire Gestures

2020 ◽  
Vol 6 (7) ◽  
pp. 1-7
Keyword(s):  
Feature Extraction ◽  
Real Time ◽  
Training Time ◽  
System A ◽  
Pose Detection ◽  
Random Weights ◽  
Majority Voter ◽  
Visual Database ◽  
Novel Method ◽  
Combined Classifier

Imagine how tiresome it is for the scorers to update the scoreboard after each ball delivery during a cricket match. They need to be alert during any point in the match, watch every single ball, record ball by ball events, modify the score and coordinate with the umpire the entire time. A system that can update the scoreboard automatically after every ball will lessen their effort by half; the time taken for the updation and the chances of errors will also be reduced. A novel method for umpire pose detection for updating the cricket scoreboard during real-time cricket matches is suggested in this work. The proposed system identifies the events happening in the pitch by recognizing the gestures of the umpire and then updates the scoreboard accordingly. The concept of transfer learning is used to accelerate the training of neural network for feature extraction. The Inception V3 network pretrained on the visual database ImageNet is culled as the primary prospect for feature extraction. Instead of initializing the model with random weights, initializing it with the pretrained weights reduces the training time and hence is more efficient. The proposed system is a combination of two SVM classifiers. The leadoff classifier tells apart the images that contain an umpire from the non-umpire images. These ‘umpire’ images are then carried forward to the event detection classifier while the ‘non-umpire’ images are repudiated. The second classifier is able to identify four gestures – ‘Six’, ‘Wide’, ‘No ball’ and ‘Out’ from the images, following which the scoreboard is updated. In addition to these four classes, one more label is defined to group those umpire frames within which the umpire does not show any signal, namely the ‘No Action’ class. The cricket video given as input is first split into number of shots and each frame is considered as a test image for the combined classifier system. A majority voter is used to confirm the final classification result which decreases the chances of misclassifications. The preliminary results suggest that the intended system is efficacious for the purpose of automating the updation of scoreboard during real time cricket matches.

A Monadic Framework for Real-time 2D Multi Person Pose Detection for captured Images and Videos

2021 ◽  
Author(s):  
Challapalli Jhansi Rani ◽  
Nagaraju Devarakonda ◽  
K.W.S..N Kumari
Keyword(s):  
Real Time ◽  
Pose Detection

Multi-collaborative Regression Convolutional Neural Network for Vehicle Angle Detection

2020 ◽  
Vol 13 (8) ◽  
pp. 1227-1241
Author(s):  
Guoqiang Chen ◽  
Mengchao Liu ◽  
Hongpeng Zhou ◽  
Bingxin Bai
Keyword(s):  
Neural Network ◽  
Convolutional Neural Network ◽  
Real Time ◽  
Autonomous Vehicles ◽  
Training Sample ◽  
Detection Accuracy ◽  
Detection Error ◽  
Detection Failure ◽  
Pose Detection ◽  
Parking Lot

Background: The vehicle pose detection plays an important role in monitoring vehicle behavior and the parking situation. The real-time detection of vehicle pose with high accuracy is of great importance. Objective: The goal of the work is to construct a new network to detect the vehicle angle based on the regression Convolutional Neural Network (CNN). The main contribution is that several traditional regression CNNs are combined as the Multi-Collaborative Regression CNN (MCR-CNN), which greatly enhances the vehicle angle detection precision and eliminates the abnormal detection error. Methods: Two challenges with respect to the traditional regression CNN have been revealed in detecting the vehicle pose angle. The first challenge is the detection failure resulting from the conversion of the periodic angle to the linear angle, while the second is the big detection error if the training sample value is very small. An MCR-CNN is proposed to solve the first challenge. And a 2- stage method is proposed to solve the second challenge. The architecture of the MCR-CNN is designed in detail. After the training and testing data sets are constructed, the MCR-CNN is trained and tested for vehicle angle detection. Results: The experimental results show that the testing samples with the error below 4° account for 95% of the total testing samples based on the proposed MCR-CNN. The MCR-CNN has significant advantages over the traditional vehicle pose detection method. Conclusion: The proposed MCR-CNN cannot only detect the vehicle angle in real-time, but also has a very high detection accuracy and robustness. The proposed approach can be used for autonomous vehicles and monitoring of the parking lot.

Pose detection of cameras in real time

2003 ◽  
Author(s):  
Julian R. Cozar ◽  
Nicolas Guil ◽  
Emilio L. Zapata
Keyword(s):  
Real Time ◽  
Pose Detection
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