I have a robotic system to develop, in first phase of the project I need to track an object. So I placed a geometrical marker on the object to estimate it's pose (rotation, translation). It all works fine until the target object moves a little faster and motion blur is introduced to the input images from camera.
So I have two options:
- Either deconvolve the images to remove blur before object tracking
- OR use a camera with very high frames per second
The problem is first solution is too slow for a real-time system and second option is too expensive.
Please tell me if there is a more efficient technique than older ones (Weiner, Lucy-Richardson, Blind Deconvolution) to remove motion blur. I believe there must be something, because mobile robot need real-time calculations from their camera inputs and motion blur is a common problem when robot or the target object is moving.
I'm using Python 2.7 with Opencv 3 and ROS Kinetic.
Otherwise let me know how many fps are sufficient to observe a human who can walk, run, fall from a distance of 10-20 ft. I can't go and buy a 400-1000 fps camera just to check the output.
Below are the images of some sample markers.