The increasing popularity of 3D TV creates the desire for more 3D video content. Unfortunately, it will take much time
for there to be an abundance of 3D video content derived from stereoscopic cameras. However, there currently exists a
vast quantity of 2D video material that can potentially be converted to 3D. Converting 2D into 3D is a complex process,
and so can be costly. Thus, an automated solution that can be achieved with low-complexity would be desirable. Our
past research work has already resulted in a real-time 2D-to-3D conversion technique, but this generates a surrogate
depth map that results in pseudo-3D and not necessarily accurate 3D. Our current research focuses on improving the
accuracy of the 3D effect by implementing a technique composed of a multi-step process to determine the depth-order of
objects, with respect to the camera, in each frame of a video sequence, and incorporating into our existing technique.
The multi-step process can be summarized as follows: detect pixels that belong to an edge; use block-based motion
estimation to determine if an edge pixel is moving and thus belongs to a moving edge (i.e., occlusion boundary);
determine which of either the left or right side block moves with the moving edge pixel, and by deduction determines the
occluding object; select seed points from the moving edge pixels; implement color-only region growing from each seed;
cluster regions into objects based on their proximity; globally assign depth-order to the objects based on perceived
viewing perspective of a frame; and modify the original surrogate depth map to create a more accurate depth map. Test
results show that this is a very effective and fast technique for deriving the depth-order of objects and generating more
accurate depth map values.