Motion capture of hands in action using discriminative salient points
ECCV'12 Proceedings of the 12th European conference on Computer Vision - Volume Part VI
Hand posture recognition from disparity cost map
ACCV'12 Proceedings of the 11th Asian conference on Computer Vision - Volume Part II
Imaginary devices: gesture-based interaction mimicking traditional input devices
Proceedings of the 15th international conference on Human-computer interaction with mobile devices and services
Model-based 3D tracking of an articulated hand from single depth images
Pattern Recognition Letters
Dynamic 2D/3D registration for the Kinect
ACM SIGGRAPH 2013 Courses
Robust hand tracking in realtime using a single head-mounted RGB camera
HCI'13 Proceedings of the 15th international conference on Human-Computer Interaction: interaction modalities and techniques - Volume Part IV
Using kinect for 2D and 3D pointing tasks: performance evaluation
HCI'13 Proceedings of the 15th international conference on Human-Computer Interaction: interaction modalities and techniques - Volume Part IV
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We propose a method that relies on markerless visual observations to track the full articulation of two hands that interact with each-other in a complex, unconstrained manner. We formulate this as an optimization problem whose 54-dimensional parameter space represents all possible configurations of two hands, each represented as a kinematic structure with 26 Degrees of Freedom (DoFs). To solve this problem, we employ Particle Swarm Optimization (PSO), an evolutionary, stochastic optimization method with the objective of finding the two-hands configuration that best explains observations provided by an RGB-D sensor. To the best of our knowledge, the proposed method is the first to attempt and achieve the articulated motion tracking of two strongly interacting hands. Extensive quantitative and qualitative experiments with simulated and real world image sequences demonstrate that an accurate and efficient solution of this problem is indeed feasible.