Detection of point targets amid cluttered background in IR imagery for IRST and MAWS applications

Due to its relevance in a variety of aerial surveillance and countermeasure systems, detecting point targets against a cluttered backdrop in infrared images has always remained an important topic of research. We evaluate the performance of six point target detection algorithms- Top hat morphology, Modified Top Hat Morphology, Contour Morphology, Method of Directional derivative, Max-Mean and Max-Median- for a synthetic IR video dataset comprising of point targets following predefined trajectories amid cluttered background. True positive rate (Probability of detection) and false alarm rate averaged over all the video frames have been considered as performance measure. It is found that for all the algorithms considered there is a trade off between the True Positive and False Alarm Rate.