Foreground segmentation for video frames plays an important role in video
surveillance, pattern recognition, video indexing, and video coding. Due to the
large amount of video data, videos have to be compressed before storage and
transmission. Foreground segmentation based on the compressed information
prevents from processing the original frame, therefore, is an algorithm
suitable for real-time systems.
In recent years, video compression standards had been promoted rapidly. In
the H.264/AVC video coding standard, in addition to motion vectors, there are
also seven-mode block partitions which can provide extra information for
segmentation. In the proposed algorithm for moving object segmentation of video
acquired by moving cameras, we first approximate the relative global motion
model using motion vectors, then mark the blocks with motion vectors differed
from the global motion by a certain amount as foreground blocks. During the
procedure described above, according to the different block partition modes, we can choose preferable MVs
for estimating the global motion, moreover, improve the accuracy of the
judgments on blocks as being foreground or background. Finally, we refine the
results with spatial and temporal filters for removing noises and segment
foreground blocks by the proposed adaptive threshold.
With the use of motion vectors, we can process the video data for moving
cameras in the compressed domain. It makes this algorithm more practical in
applications rather than many object segmentation methods using spatial domain
information. It also reduces the computational costs and can be used for
real-time systems.