Using train_cascade - LBP, I am trying to detect a brand name in a outdoor event - captured via video. The brand Name can be in the following orientations. 1) Straight in an upright manner on a vertical board.
2) Can be on the painted on the ground. So, when an upright camera focuses on it, the angle of view is slightly different and the Brandname could appear to be larger and a little stretchy.
3) Can be on the ground With the logo/Brand Name rotated by say, 70 - 80 degrees. Say, if the Brand Name is "MYBRANDNAME". Imagine this on the ground with M starting at top left corner and ending with E in the middle of the screen (kind of orientation). Another variation of this could be starting from the center of the screen to the top right corner.
4) Also, when this Brand name is shot from a chopper or from the top of a high rise, there could be numerous zoom possibilities.
Above are the scenarios. Questions are as below: 1) I am able to handle #1 in most cases. So, no issues there. 2) Will the training for #1 be able to detect the other ones in #2? 3) I assume cascade training is not Rot-scale invariant. So, The same cascade may not work for #3 and #4. So, what is the best way to approach this training? How many samples do you think I might need to get a good cascade. 4) Finally, If I extract images from a video of this event, should I use consecutive frames as positives? They all will have a slowly changing size/angle and for a human eye, the first and last frame could be different, but the in between frames will be almost similar. So, is it worthwhile to use all those frames? Or since the consecutive frames are very similar, it is best to disregard some of them.
Thanks in advance for your responses.
samjakar