scholarly journals Real Time 3D Facial Movement Tracking Using a Monocular Camera

Sensors ◽  
2016 ◽  
Vol 16 (8) ◽  
pp. 1157 ◽  
Author(s):  
Yanchao Dong ◽  
Yanming Wang ◽  
Jiguang Yue ◽  
Zhencheng Hu
Keyword(s):  
Real Time ◽  
Facial Movement ◽  
Monocular Camera ◽  
Movement Tracking

Robust Real-Time Bio-Kinematic Movement Tracking Using Multiple Kinects for Tele-Rehabilitation

2016 ◽  
Vol 63 (3) ◽  
pp. 1822-1833 ◽  
Author(s):  
Pubudu N. Pathirana ◽  
Saiyi Li ◽  
Hieu M. Trinh ◽  
Aruna Seneviratne
Keyword(s):  
Real Time ◽  
Movement Tracking

Real-time fish animation generation by monocular camera

2018 ◽  
Vol 71 ◽  
pp. 55-65 ◽  
Author(s):  
Xiangfei Meng ◽  
Junjun Pan ◽  
Hong Qin ◽  
Pu Ge
Keyword(s):  
Real Time ◽  
Monocular Camera ◽  
Animation Generation

scholarly journals Object Detection and Movement Tracking Using Tubelets and Faster RCNN Algorithm with Anchor Generation

2021 ◽  
Vol 2021 ◽  
pp. 1-16
Author(s):  
Prabu Mohandas ◽  
Jerline Sheebha Anni ◽  
Rajkumar Thanasekaran ◽  
Khairunnisa Hasikin ◽  
Muhammad Mokhzaini Azizan
Keyword(s):  
Object Detection ◽  
Real Time ◽  
Tracking System ◽  
Detection And Tracking ◽  
Occluded Objects ◽  
Object Movement ◽  
Localization Errors ◽  
Crop Damages ◽  
Improved Performance ◽  
Movement Tracking

Object detection in images and videos has become an important task in computer vision. It has been a challenging task due to misclassification and localization errors. The proposed approach explored the feasibility of automated detection and tracking of elephant intrusion along forest border areas. Due to an alarming increase in crop damages resulted from movements of elephant herds, combined with high risk of elephant extinction due to human activities, this paper looked into an efficient solution through elephant’s tracking. The convolutional neural network with transfer learning is used as the model for object classification and feature extraction. A new tracking system using automated tubelet generation and anchor generation methods in combination with faster RCNN was developed and tested on 5,482 video sequences. Real-time video taken for analysis consisted of heavily occluded objects such as trees and animals. Tubelet generated from each video sequence with intersection over union (IoU) thresholds have been effective in tracking the elephant object movement in the forest areas. The proposed work has been compared with other state-of-the-art techniques, namely, faster RCNN, YOLO v3, and HyperNet. Experimental results on the real-time dataset show that the proposed work achieves an improved performance of 73.9% in detecting and tracking of objects, which outperformed the existing approaches.

scholarly journals GUIDING AND LOCALISING IN REAL-TIME A MOBILE ROBOT WITH A MONOCULAR CAMERA IN NON-FLAT TERRAINS

2007 ◽  
Vol 40 (15) ◽  
pp. 560-565 ◽  
Author(s):  
Teresa Vidal-Calleja ◽  
Alberto Sanfeliu ◽  
Juan Andrade-Cetto
Keyword(s):  
Mobile Robot ◽  
Real Time ◽  
Monocular Camera
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