Clustered Stochastic Optimization for Object Recognition and Pose Estimation

We present an approach for estimating the 3D position and in case of articulated objects also the joint configuration from segmented 2D images. The pose estimation without initial information is a challenging optimization problem in a high dimensional space and is essential for texture acquisition and initialization of model-based tracking algorithms. Our method is able to recognize the correct object in the case of multiple objects and estimates its pose with a high accuracy. The key component is a particle-based global optimization method that converges to the global minimum similar to simulated annealing. After detecting potential bounded subsets of the search space, the particles are divided into clusters and migrate to the most attractive cluster as the time increases. The performance of our approach is verified by means of real scenes and a quantative error analysis for image distortions. Our experiments include rigid bodies and full human bodies.