PROCEEDINGS ARTICLE | November 15, 2007
Proc. SPIE. 6786, MIPPR 2007: Automatic Target Recognition and Image Analysis; and Multispectral Image Acquisition
KEYWORDS: Signal to noise ratio, Detection and tracking algorithms, Cameras, Image processing, Fourier transforms, Image analysis, Image registration, Light sources and illumination, Image filtering, Astronomical engineering
This paper proposes a method used to detect big moving object in the complicated dynamic background, which integrates
the phase correlation technique including singular value decomposition and the method in which multi-frames difference
images is multiplied. The phase correlation algorithm based on singular value decomposition is insensitive to noise and
change of gray and contrast. Comparing with many complex phase correlation algorithm and registration algorithm in
spatial domain, our method not only can effectively restrain noise, but also enhancing the registration precision, whose
speed is nearly two times as quickly as original phase correlation algorithm. The fact is found by the result of experiment
that the phase correlation matrix is rank one for a noise-free rigid translation model. A new phase correlation matrix is
recast based on the property which can effectively restrain noise and change of gray. By estimating global moving vector
of two images using phase correlation based on singular value decomposition, background is accurately matched. The
matched images are processed to calculate the image differences between the first and fourth, the second and fifth, the third
and sixth. After these difference images are multiplied, clear edge of moving object is obtained. Thus the accurate location
of object is realized by calculating barycentre of image. At last, simulation results prove that this proposed method can
overcome effectiveness well in the lighting variations and noise. It is also efficient and applicable for accurate moving
object orientation in the complicated dynamic background.
