It sparsely samples the pose space around the measured pose and for a few promising candidates applies a robust optimization procedure.
Tracking unknown human motions using generative tracking techniques requires the exploration of a high-dimensional pose space which is both difficult and computationally expensive.
In this case, the relationship is defined by learning continuous mapping functions between the face image and the pose space [29 31].
In our experiments, we evaluate different numbers of Gaussian processes (recall that we use one Gaussian process for each dimension in our weak pose space).
With the discretion of the time t and camera pose space, the integration can be rewritten as the weighted summation of all homographies in the discrete camera pose space, where a homography is the transformed sharp image projected by corresponding camera pose.
The resulting algorithm, which we term multiple activity model annealed particle filtering (MAM-APF), is able to dynamically vary the scope of its search effort, using a small number of particles to explore latent pose spaces and a large number of particles to explore the full pose space.
With predicted poses from image features, we map the problem from image feature space to pose space, where a Bag of Poses (BOP) model is learned for the final goal of HAR.
We can observe that the reference poses obtained sample the 3D pose space in a salient manner, while drastically reducing the variability of poses present in a given gesture.
Alternatively, if the type of activity is known and training data is available, a low-dimensional latent pose space may be learned and the difficulty and cost of the estimation task reduced.
We define a number of "activity models", each composed of a pose space with unique dimensionality and an associated dynamical model, and each designed for use in the recovery of a particular class of activity.
