Detecting small moving objects using temporal hypothesis testing

This paper addresses the problem of detecting small, moving, low amplitude objects in image sequences that also contain moving nuisance objects and background noise. We formulate this problem in the context of a hypothesis testing procedure on individual pixel temporal profiles, leading to a computationally efficient statistical test. The technique assumes we have reasonable deterministic and statistical models for the temporal behavior of the background noise, target, and clutter, on a single pixel basis. Based on these models we develop a generalized likelihood ratio test (GLRT) and perfect measurement performance analysis, and present the resulting decision rule. We also propose a parameter estimation technique and compare its performance to the Cramer Rao bound (CRB). We demonstrate the effectiveness of the technique by applying the resulting algorithm to real world infrared (IR) image sequences containing targets of opportunity. The approach could also be applicable to other image sequence processing scenarios, using acquisition systems besides IR imaging, such as detection of small moving objects or structures in a biomedical or biological imaging scenario, or the detection of satellites, meteors or other celestial bodies in night sky imagery acquired using a telescope.