Probabilistic detection and tracking of IR targets

The problem of automatic target recognition (ATR) and image classification have been active research fields in image processing. In this research, we explore ATR techniques such as object pre-processing, detection, tracking and classification for sequence of infrared (IR) images. The detection and tracking of IR images is performed using Bayesian probabilistic technique. The tracked part of the object frame is then processed to discard the background to obtain just the segmented object. The segmented dataset is then rendered shift invariant by first calculating the mean of the object and then moving the mean to center of the frame. We divide each frame into blocks and obtain statistical features such as mean, variance, minimum and maximum intensity in each block for subsequent classification. We visually divide entire IR dataset into 8 classes for supervised training using a K-nearest neighbor classifier. We classify the test IR dataset into 8 different classes successfully.