The Stixel World is a medium-level, compact representation of road scenes that abstracts millions of disparity pixels into hundreds or thousands of stixels. The goal of this work is to implement and evaluate a complete multi-stixel estimation pipeline on an embedded, energy-efficient, GPU-accelerated device. This work presents a full GPU-accelerated implementation of stixel estimation that produces reliable results at 26 frames per second (real-time) on the Tegra X1 for disparity images of 1024x440 pixels and stixel widths of 5 pixels, and achieves more than 400 frames per second on a high-end Titan X GPU card.
In order to achieve real-time stixel estimation on CPU, @cite_19 developed a less accurate method to calculate the ground surface by accumulating matching costs into vertical-disparity space, and then computing the bottom and height of stixels in disparity-cost space. Disparities are computed only for object stixels and not for ground.
@cite_19
2047166686
Mobile robots require object detection and classification for safe and smooth navigation. Stereo vision improves such detection by doubling the views of the scene and by giving indirect access to depth information. This depth information can also be used to reduce the set of candidate detection windows. Up to now, most algorithms compute a depth map to discard unpromising detection windows. We propose a novel approach where a stixel world model is computed directly from the stereo images, without computing an intermediate depth map. We experimentally demonstrate that such approach can considerably reduce the set of candidate detection windows at a fraction of the computation cost of previous approaches.