Infrared (IR) imagery sequences are commonly used for detecting moving targets in the presence of evolving cloud
clutter or background noise. This research focuses on slow moving point targets that are less than one pixel in size, such
as aircraft at long ranges from a sensor.
The target detection performance is measured via the variance estimation ratio score (VERS), which essentially
calculates the pixel scores of the sequences, where a high score indicates a target is suspected to traverse the pixel. VERS
uses two parameters – long and short term windows, which were predetermined individually for each movie, depending
on the target velocity and on the clouds intensity and amount, as opposed to clear sky (noise), in the background. In this
work, we examine the correlation between the sequences' spatial and temporal features and these two windows. In
addition, we modify VERS calculation, to enhance target detection and decrease cloud-edge scores and false detection.
We conclude this work by evaluating VERS as a detection measure, using its original version and its modified version.
The test sequences are both original real IR sequences as well as their relative compressed sequences using our
designated temporal DCT quantization method.