3-D Recovery of a Non-rigid Object from a Single Camera View Employing Multiple Coordinates Representation

2013 ◽  
Author(s):  
Shota Ishikawa ◽  
Joo Kooi Tan ◽  
Hyoungseop Kim ◽  
Seiji Ishikawa
Keyword(s):  
Single Camera ◽  
Rigid Object ◽  
Camera View

Measuring Turning Trajectory of 4WD-4WS Vehicle using Single Camera View

2012 ◽  
Vol 5 (1) ◽  
pp. 36-41 ◽  
Author(s):  
Takashi Fukushima ◽  
Kunio Sato ◽  
Shou Yan Zhao ◽  
Keisuke Kimura ◽  
Shunsuke Mizutani
Keyword(s):  
Single Camera ◽  
Camera View

scholarly journals Towards a better understanding of the overall health impact of the game of squash: automatic and high-resolution motion analysis from a single camera view

2017 ◽  
Vol 3 (2) ◽  
pp. 819-823
Author(s):  
Christopher Brumann ◽  
Markus Kukuk
Keyword(s):  
High Resolution ◽  
Motion Analysis ◽  
Gaussian Mixture ◽  
Health Impact ◽  
Video Frame ◽  
Single Camera ◽  
Total Occlusion ◽  
Heat Maps ◽  
Motion Patterns ◽  
Camera View

AbstractIn this paper, we present a method for locating and tracking players in the game of squash using Gaussian mixture model background subtraction and agglomerative contour clustering from a calibrated single camera view. Furthermore, we describe a method for player re-identification after near total occlusion, based on stored color- and region-descriptors. For camera calibration, no additional pattern is needed, as the squash court itself can serve as a 3D calibration object. In order to exclude non-rally situations from motion analysis, we further classify each video frame into game phases using a multilayer perceptron. By considering a player’s position as well as the current game phase we are able to visualize player-individual motion patterns expressed as court coverage using pseudo colored heat-maps. In total, we analyzed two matches (six games, 1:28h) of high quality commercial videos used in sports broadcasting and compute high resolution (1cm per pixel) heat-maps. 130184 manually labeled frames (game phases and player identification) show an identification correctness of 79.28±8.99% (mean±std). Game phase classification is correct in 60.87±7.62% and the heat-map visualization correctness is 72.47±7.27%.

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