Visual tracking and motion determination using the IMM algorithm

We present a feature tracking system with automatic motion determination of features in an image sequence. The positions of features (corners) extracted in the first frame of a sequence are estimated and predicted in the subsequent frames by using an extension of Bayesian multiple hypothesis technique (MHT) based on different motion models. The tracking of features is based on the interacting multiple model (IMM). The paper shows how the IMM algorithm combined with a MHT framework can be used in a visual tracking scenario. We considered different order (types) velocity and acceleration models for the IMM algorithm and applied them to two image sequences, the PUMA sequence and toy car sequence. The study shows that the method proposed can distinguish between different motions depicted in an image sequence with very good tracking results