Abstract New method of the human body pose estimation based on a single camera 2D observation is presented, aimed at smart surveillance related video analysis and action recognition. It employs 3D model of the human body, and genetic algorithm combined with annealed particle filter for searching the global optimum of model state, best matching the object's 2D observation. Additionally, new motion cost metric is employed, considering current pose and history of the body movement, favouring the estimates with the lowest changes of motion speed comparing to previous poses. The "genetic memory" concept is introduced for the genetic processing of both current and past states of 3D model. State-of-the-art in the field of human body tracking is presented and discussed. Details of implemented method are described. Results of experimental evaluation of developed algorithm are included and discussed.
In REF ), a Hybrid genetic-APF method is presented in which a genetic algorithm is combined with annealed PF for human body pose estimation.
Genetic programming extension to APF-based monocular human body pose estimation
Multimedia Tools and Applications
Multimedia Tools and Applications
Computer Science