Omnidirectional tracking and recognition of persons in planar views

In this paper a view-independent head tracking system applying an Active Shape Model based particle filter is used to find precise image sections. DCTmod2 feature sequences are extracted from these sections and given as input to Cyclic Pseudo two-dimensional Hidden Markov Model based classifiers. These classifiers are trained to recognize the identity of the shown persons. The video material is recorded in an office environment with changing lighting conditions and thus results in a challenging task for both tracking and recognition. The overall performance of the system is evaluated depending on the various views of persons rotating on a swivel chair.