scholarly journals Independent 3D motion detection based on depth elimination in normal flow fields

2002 ◽  
Author(s):  
A.A. Argyros ◽  
S.C. Orphanoudakis
Keyword(s):  
Motion Detection ◽  
Flow Fields ◽  
Normal Flow ◽  
3D Motion

Bionic compound eye for 3D motion detection using an optical freeform surface

2017 ◽  
Vol 34 (5) ◽  
pp. B28 ◽  
Author(s):  
Kuo Pang ◽  
Fengzhou Fang ◽  
Le Song ◽  
Yue Zhang ◽  
Haoyang Zhang
Keyword(s):  
Motion Detection ◽  
Compound Eye ◽  
Freeform Surface ◽  
3D Motion

Simultaneous 3D Motion Detection, Long-Term Tracking and Model Reconstruction for Multi-Objects

2019 ◽  
Vol 16 (04) ◽  
pp. 1950017
Author(s):  
Sheng Liu ◽  
Yangqing Wang ◽  
Fengji Dai ◽  
Jingxiang Yu
Keyword(s):  
3D Reconstruction ◽  
Object Tracking ◽  
Motion Detection ◽  
Visual Information ◽  
Detection Accuracy ◽  
Human Machine Interaction ◽  
Reconstruction Process ◽  
3D Motion ◽  
Machine Interaction

Motion detection and object tracking play important roles in unsupervised human–machine interaction systems. Nevertheless, the human–machine interaction would become invalid when the system fails to detect the scene objects correctly due to occlusion and limited field of view. Thus, robust long-term tracking of scene objects is vital. In this paper, we present a 3D motion detection and long-term tracking system with simultaneous 3D reconstruction of dynamic objects. In order to achieve the high precision motion detection, an optimization framework with a novel motion pose estimation energy function is provided in the proposed method by which the 3D motion pose of each object can be estimated independently. We also develop an accurate object-tracking method which combines 2D visual information and depth. We incorporate a novel boundary-optimization segmentation based on 2D visual information and depth to improve the robustness of tracking significantly. Besides, we also introduce a new fusion and updating strategy in the 3D reconstruction process. This strategy brings higher robustness to 3D motion detection. Experiments results show that, for synthetic sequences, the root-mean-square error (RMSE) of our system is much smaller than Co-Fusion (CF); our system performs extremely well in 3D motion detection accuracy. In the case of occlusion or out-of-view on real scene data, CF will suffer the loss of tracking or object-label changing, by contrast, our system can always keep the robust tracking and maintain the correct labels for each dynamic object. Therefore, our system is robust to occlusion and out-of-view application scenarios.

Recurrence of fragmenting granules and their relation to meso- and supergranular flow fields

2003 ◽  
Vol 9 ◽  
pp. 371-371
Author(s):  
Th. Roudier ◽  
F. Lignières ◽  
M. Rieutord ◽  
P. N. Brandt ◽  
J.-M. Malherbe
Keyword(s):  
Flow Fields

Cooking Motion Detection by Using Kinect

2016 ◽  
Vol 136 (12) ◽  
pp. 1759-1760
Author(s):  
Masao Izumi ◽  
Kenji Hashimoto
Keyword(s):  
Motion Detection
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