| 3D human pose estimation is an important field in Computer Vision. It has a wide range of applications, such as human-computer interaction, intelligent animation synthesis, video surveillance, etc. Single camera video, due to the lack of depth information, causes difficult challenges of estimating 3D human pose. This paper proposes a modified particle swarm optimization method combined with human motion prior knowledge in order to achieve a robust analysis-via-synthesis strategy. Due to the numerous applications of human upper body movements, we are focusing on creating a front-view human upper body model. Due to the high dimensional body configuration of human pose estimation, particle swarm optimization, with great global search ability, has a very slow convergence speed. Therefore, our modified algorithm uses annealing method so that the particles can converge faster to the lowest likelihood function value. This fact makes our algorithm more effective. Integrated use of several image features, such as silhouette, arm silhouette, ratio silhouette area, edge, motion and skin color, constructs our cost function. Each feature has its unique purpose in order to achieve much more accurate and robust pose estimation results. Constraining human body configuration, including the perspective scope of joint movements angle range constraints and non-penetrating constraints of limbs, is to make sure estimating human pose in the feasible region, preventing illegal pose data, and improve the accuracy of 3D human tracking. In addition, a trajectory feature is used to re-distribute particles for every frame tracking. Experiment results show that our modified algorithm combined with cost function provides a much more accurate and robust result than downhill simplex algorithm [1] and Annealing Particle Swarm Optimization Particle Filter [2]. |
