Are there some genaral rules for the robustness between monocular and stereo vision when considering object detection? I am especially interested in the automotive field - considering distance/obstacle/car detection (see video links below).
Someone told me monocular vision is more robust than stereo. I guess this may be true if the monocular algorithm is well written (and especially verified over lots of input data)... but once you input (image) data that has not been verified it may probably provide unexpected results, right? With stereo vision one does not really care about the contents of the image as long as texture/lighting conditions allow stereo matching and the object detection is then done within the point cloud.
I consider following usage:
The monocular sample video seems to have sometimes problems detecting the cars in front (the bounding boxes disappear once in a while). The stereo sample seems to be more robust - the car in front clearly is detected in all of sequnce image frames.