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An evaluation framework for stereo-based driver assistance
This is the post-print version of the Article - Copyright @ 2012 Springer VerlagThe accuracy of stereo algorithms or optical flow methods is commonly assessed by comparing the results against the Middlebury
database. However, equivalent data for automotive or robotics applications
rarely exist as they are difficult to obtain. As our main contribution, we introduce an evaluation framework tailored for stereo-based driver assistance able to deliver excellent performance measures while
circumventing manual label effort. Within this framework one can combine several ways of ground-truthing, different comparison metrics, and use large image databases.
Using our framework we show examples on several types of ground truthing techniques: implicit ground truthing (e.g. sequence recorded without a crash occurred), robotic vehicles with high precision sensors, and to a small extent, manual labeling. To show the effectiveness of our evaluation framework we compare three different stereo algorithms on
pixel and object level. In more detail we evaluate an intermediate representation
called the Stixel World. Besides evaluating the accuracy of the Stixels, we investigate the completeness (equivalent to the detection rate) of the StixelWorld vs. the number of phantom Stixels. Among many findings, using this framework enables us to reduce the number of phantom Stixels by a factor of three compared to the base parametrization. This base parametrization has already been optimized by test driving vehicles for distances exceeding 10000 km
Effects of Ground Manifold Modeling on the Accuracy of Stixel Calculations
This paper highlights the role of ground manifold modeling for stixel calculations; stixels are medium-level data representations used for the development of computer vision modules for self-driving cars. By using single-disparity maps and simplifying ground manifold models, calculated stixels may suffer from noise, inconsistency, and false-detection rates for obstacles, especially in challenging datasets. Stixel calculations can be improved with respect to accuracy and robustness by using more adaptive ground manifold approximations. A comparative study of stixel results, obtained for different ground-manifold models (e.g., plane-fitting, line-fitting in v-disparities or polynomial approximation, and graph cut), defines the main part of this paper. This paper also considers the use of trinocular stereo vision and shows that this provides options to enhance stixel results, compared with the binocular recording. Comprehensive experiments are performed on two publicly available challenging datasets. We also use a novel way for comparing calculated stixels with ground truth. We compare depth information, as given by extracted stixels, with ground-truth depth, provided by depth measurements using a highly accurate LiDAR range sensor (as available in one of the public datasets). We evaluate the accuracy of four different ground-manifold methods. The experimental results also include quantitative evaluations of the tradeoff between accuracy and run time. As a result, the proposed trinocular recording together with graph-cut estimation of ground manifolds appears to be a recommended way, also considering challenging weather and lighting conditions
Slanted Stixels: A way to represent steep streets
This work presents and evaluates a novel compact scene representation based
on Stixels that infers geometric and semantic information. Our approach
overcomes the previous rather restrictive geometric assumptions for Stixels by
introducing a novel depth model to account for non-flat roads and slanted
objects. Both semantic and depth cues are used jointly to infer the scene
representation in a sound global energy minimization formulation.
Furthermore, a novel approximation scheme is introduced in order to
significantly reduce the computational complexity of the Stixel algorithm, and
then achieve real-time computation capabilities. The idea is to first perform
an over-segmentation of the image, discarding the unlikely Stixel cuts, and
apply the algorithm only on the remaining Stixel cuts. This work presents a
novel over-segmentation strategy based on a Fully Convolutional Network (FCN),
which outperforms an approach based on using local extrema of the disparity
map.
We evaluate the proposed methods in terms of semantic and geometric accuracy
as well as run-time on four publicly available benchmark datasets. Our approach
maintains accuracy on flat road scene datasets while improving substantially on
a novel non-flat road dataset.Comment: Journal preprint (published in IJCV 2019:
https://link.springer.com/article/10.1007/s11263-019-01226-9). arXiv admin
note: text overlap with arXiv:1707.0539
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