We propose a novel hybrid approach to static pose estimation called Connected Poselets. This representation combines the best aspects of part-based and example-based estimation. First detecting poselets extracted from the training data; our method then applies a modified Random Decision Forest to identify Poselet activations. By combining keypoint predictions from poselet activitions within a graphical model, we can infer the marginal distribution over each keypoint without any kinematic constraints. Our approach is demonstrated on a new publicly available dataset with promising results.
REF proposed the use of Poselets as a representation that combines part-based and example-based estimation aspects for human pose estimation.
Putting the pieces together: Connected Poselets for human pose estimation
2011 IEEE International Conference on Computer Vision Workshops (ICCV Workshops)
2011 IEEE International Conference on Computer Vision Workshops (ICCV Workshops)
Computer Science