Combining PGMs and Discriminative Models for Upper Body Pose Detection

In this project, I utilized probabilistic graphical models together with discriminative models such as SVM to perform upper body pose estimation. Figure 1 illustrates the definition of a problem in terms of input and output. Pose estimation is an important problem that has a wide array of applications such as pedestrian detection, sports video analysis, action recognition, etc. The entire human body can be seen as a graph where each body part is the node in the graph and two adjacent body parts are connected via an edge. Therefore, it is natural to utilize probabilistic graphical models to solve pose detection related problems. Additionally, the idea of incorporating discriminative models has been shown to be very successful as well, specifically in [1] [2]. Intuitively it makes sense that combination of generative and discriminative models should produce a model that is more powerful than each of these individually. Therefore, my hope is that combining these two techniques will produce solid results for this challenging problem.