73 research outputs found
Towards End-to-End Lane Detection: an Instance Segmentation Approach
Modern cars are incorporating an increasing number of driver assist features,
among which automatic lane keeping. The latter allows the car to properly
position itself within the road lanes, which is also crucial for any subsequent
lane departure or trajectory planning decision in fully autonomous cars.
Traditional lane detection methods rely on a combination of highly-specialized,
hand-crafted features and heuristics, usually followed by post-processing
techniques, that are computationally expensive and prone to scalability due to
road scene variations. More recent approaches leverage deep learning models,
trained for pixel-wise lane segmentation, even when no markings are present in
the image due to their big receptive field. Despite their advantages, these
methods are limited to detecting a pre-defined, fixed number of lanes, e.g.
ego-lanes, and can not cope with lane changes. In this paper, we go beyond the
aforementioned limitations and propose to cast the lane detection problem as an
instance segmentation problem - in which each lane forms its own instance -
that can be trained end-to-end. To parametrize the segmented lane instances
before fitting the lane, we further propose to apply a learned perspective
transformation, conditioned on the image, in contrast to a fixed "bird's-eye
view" transformation. By doing so, we ensure a lane fitting which is robust
against road plane changes, unlike existing approaches that rely on a fixed,
pre-defined transformation. In summary, we propose a fast lane detection
algorithm, running at 50 fps, which can handle a variable number of lanes and
cope with lane changes. We verify our method on the tuSimple dataset and
achieve competitive results
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End-to-end Lane Detection through Differentiable Least-Squares Fitting
Lane detection is typically tackled with a two-step pipeline in which a
segmentation mask of the lane markings is predicted first, and a lane line
model (like a parabola or spline) is fitted to the post-processed mask next.
The problem with such a two-step approach is that the parameters of the network
are not optimized for the true task of interest (estimating the lane curvature
parameters) but for a proxy task (segmenting the lane markings), resulting in
sub-optimal performance. In this work, we propose a method to train a lane
detector in an end-to-end manner, directly regressing the lane parameters. The
architecture consists of two components: a deep network that predicts a
segmentation-like weight map for each lane line, and a differentiable
least-squares fitting module that returns for each map the parameters of the
best-fitting curve in the weighted least-squares sense. These parameters can
subsequently be supervised with a loss function of choice. Our method relies on
the observation that it is possible to backpropagate through a least-squares
fitting procedure. This leads to an end-to-end method where the features are
optimized for the true task of interest: the network implicitly learns to
generate features that prevent instabilities during the model fitting step, as
opposed to two-step pipelines that need to handle outliers with heuristics.
Additionally, the system is not just a black box but offers a degree of
interpretability because the intermediately generated segmentation-like weight
maps can be inspected and visualized. Code and a video is available at
github.com/wvangansbeke/LaneDetection_End2End.Comment: Accepted at ICCVW 2019 (CVRSUAD-Road Scene Understanding and
Autonomous Driving
Contrastive Learning for Multi-Object Tracking with Transformers
The DEtection TRansformer (DETR) opened new possibilities for object
detection by modeling it as a translation task: converting image features into
object-level representations. Previous works typically add expensive modules to
DETR to perform Multi-Object Tracking (MOT), resulting in more complicated
architectures. We instead show how DETR can be turned into a MOT model by
employing an instance-level contrastive loss, a revised sampling strategy and a
lightweight assignment method. Our training scheme learns object appearances
while preserving detection capabilities and with little overhead. Its
performance surpasses the previous state-of-the-art by +2.6 mMOTA on the
challenging BDD100K dataset and is comparable to existing transformer-based
methods on the MOT17 dataset.Comment: WACV 202
Conservation, IR, UV and 3d-imaging :the Egyptian execration statuettes project (EES) : final report
Pixel+ : visualising our heritage
In recent years more advanced imaging techniques have been introduced to study, document, curate and preserve our heritage. Pixel+ focuses on two of them: Reflectance Transformation Imaging/Polynomial Texture Mapping and the Portable Light Dome
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