3 ms·

This doesn’t account for the motion of other objects.

• NoToP 4y ago
  It does because the motion of the other objects is embedded in their position over the previous few frames. On the contrary, it is precisely filtering on moving objects such that the velocity vector is directly on a collision path with the camera. Objects which are moving but ultimately not on a collision course will be in different locations once the perspective transform is applied and thus average out to background. Objects which are moving on a collision course relative to the observer get amplified by this simple filter (irrespective of if the motion is absolute or not relative to ground).
  • robotresearcher 4y ago
    The perspective transform needs the depth of each pixel from the camera (or equivalent 3D Cartesian coordinates of objects). An affine image transform that ignores depth won’t project objects correctly.
  • deleted 4y ago
    [deleted]
  • jeffreygoesto 4y ago
    Let's assume ego and other vehicle are on a straight lane and move with constant speed along the same line. All trajectories where the other vehicle is not faster than ego are eventually colliding. But for each velocity that other vehicle projects to different positions, doesn't it? You can accumulate for one realtive motion only, but there are many dangerous ones. Taking ego speed and steering will accumulate standstill objects on the road plane (if the homography between image and road is accounter for properly). Clearly standstill objects in your path are on a collision course but the original problem was about moving and especially decelerating ones. Also it takes a large amount of frames to really average out. Pre-ML We used to implement multi-hypothesis Kalman filters and took the one with the least invention as best prediction.