Use of a Confidence Map Towards Improved Multi-layer Stixel Segmentation
Noor Haitham Saleem, Reinhard Klette, Fay Huang · 2018
We propose the use of a reliable confidence map for multi-layer stixel segmentation; our confidence map uses a calibrated collinear trinocular vision model. It is generated from three conjugate synchronized stereo images for evaluating the consistency of disparity values. The evaluation measure is referred to as transitivity error in disparity space. Multi-layer stixels are commonly generated from a single disparity map which make them merely dependent on the applied stereo matcher. A multi-map fusion is proposed to achieve more reliable stixel segmentation for disparity values. Moreover, another advantage of our work is to provide a new and effective ground-detection technique (ground-manifold detection) which benefits from the confidence map. Experimental results show a significant improvement on average of 12.6% using our method compared with conventional stixels detected by binocular vision only.