Isomap Tracking with Particle Filtering

The problem of tracking involves challenges like in-plane and out-of-plane rotations, scaling, variations in ambient light and occlusions. In this paper we look at the problem of tracking a person´s head and also estimating its pose in each frame. Robust tracking can be achieved by reducing the dimensionality of high-dimensional training data and using the recovered low-dimensional structure to estimate the state of an object at every time-step with recursive Bayesian filtering. Isometric feature mapping, also known as Isomap, provides an unsupervised framework to find the true degrees of freedom in high-dimensional input data like a person´s head with varying poses. After the data has been reduced to lower dimensions a particle filter can be used to track and at the same time approximate the pose of a person´s head in any image sequence. Isomap tracking with particle filtering is capable of handling rapid translation and out-of-plane rotation of a person´s head with a relatively small amount of training data. The performance of the tracker is demonstrated on an image sequence with a person´s head undergoing translation and out-of-plane rotation.