Ship-based automatic detection of small floating objects on an agitated sea surface remains a hard problem. Our
main concern is the detection of floating mines, which proved a real threat to shipping in confined waterways
during the first Gulf War, but applications include salvaging,search-and-rescue and perimeter or harbour defense.
IR video was chosen for its day-and-night imaging capability, and its availability on military vessels.
Detection is difficult because a rough sea is seen as a dynamic background of moving objects with size order,
shape and temperature similar to those of the floating mine. We do find a determinant characteristic in the
target's periodic motion, which differs from that of the propagating surface waves composing the background.
The classical detection and tracking approaches give bad results when applied to this problem. While background
detection algorithms assume a quasi-static background, the sea surface is actually very dynamic, causing
this category of algorithms to fail. Kalman or particle filter algorithms on the other hand, which stress temporal
coherence, suffer from tracking loss due to occlusions and the great noise level of the image.
We propose an innovative approach. This approach uses the periodicity of the objects movement and thus its
temporal coherence. The principle is to consider the video data as a spacetime volume similar to a hyperspectral
data cube by replacing the spectral axis with a temporal axis. We can then apply algorithms developed for
hyperspectral detection problems to the detection of small floating objects.
We treat the detection problem using multilinear algebra, designing a number of finite impulse response
filters (FIR) maximizing the target response. The algorithm was applied to test footage of practice mines in the