Fusion of Kalman Filter and anomaly detection for multispectral and hyperspectral target tracking

This paper proposes a novel tracking methodology for mul-tispectral and hyperspectral sequences. Our approach combines techniques used in multispectral and hyperspectral anomaly detection with a Kalman-Filter (KF) for tracking. The algorithm takes advantage of the additional information provided by the spectra in multispectral and hyperspectral sequences and combines it with a KF to track a target in the presence of occlusions. The proposed algorithm is based on modeling the tracked object´s local background with the help of a Self Organizing Map (SOM), followed by the construction of a 2D Centre of Gravity Map (CoGM), the entries of which lead to the localisation of the target´s current position. The KF, in prediction mode, is employed in order to perform robust tracking during occlusion.