3D human model and joint parameter estimation from monocular image
Pattern Recognition Letters
Human body pose estimation with particle swarm optimisation
Evolutionary Computation
Behavior analysis with combined RFID and video information
UIC'06 Proceedings of the Third international conference on Ubiquitous Intelligence and Computing
Recover human pose from monocular image under weak perspective projection
ICCV'05 Proceedings of the 2005 international conference on Computer Vision in Human-Computer Interaction
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This paper proposes a model-based method for estimating the pose of a two-dimensional (2-D) articulated object from a single silhouette image using a genetic algorithm (GA). In this study, a human body viewed sideways is treated as a 2-D articulated object. The model of the articulated object is given as a stick one. Its pose is represented in 2-D position of the main stick and angles of all sticks connected at joints. The parameter space for representing the pose of the stick model is very large. Therefore, we limit the search space to the range in which the stick model does not protrude from the silhouette. For the search space limitation, the region of position and angle of the stick perfectly included in the silhouette is obtained for each stick in advance. The pose of the stick model is evaluated in the sum of the proximity measure from each pixel in the silhouette to the stick model. Experiments with synthetic silhouettes by a human figure design tool show that the proposed method can estimate various poses and that it can be applied to several human figures with a single stick model.