6,733 research outputs found
Towards Full Automated Drive in Urban Environments: A Demonstration in GoMentum Station, California
Each year, millions of motor vehicle traffic accidents all over the world
cause a large number of fatalities, injuries and significant material loss.
Automated Driving (AD) has potential to drastically reduce such accidents. In
this work, we focus on the technical challenges that arise from AD in urban
environments. We present the overall architecture of an AD system and describe
in detail the perception and planning modules. The AD system, built on a
modified Acura RLX, was demonstrated in a course in GoMentum Station in
California. We demonstrated autonomous handling of 4 scenarios: traffic lights,
cross-traffic at intersections, construction zones and pedestrians. The AD
vehicle displayed safe behavior and performed consistently in repeated
demonstrations with slight variations in conditions. Overall, we completed 44
runs, encompassing 110km of automated driving with only 3 cases where the
driver intervened the control of the vehicle, mostly due to error in GPS
positioning. Our demonstration showed that robust and consistent behavior in
urban scenarios is possible, yet more investigation is necessary for full scale
roll-out on public roads.Comment: Accepted to Intelligent Vehicles Conference (IV 2017
Deep Interactive Region Segmentation and Captioning
With recent innovations in dense image captioning, it is now possible to
describe every object of the scene with a caption while objects are determined
by bounding boxes. However, interpretation of such an output is not trivial due
to the existence of many overlapping bounding boxes. Furthermore, in current
captioning frameworks, the user is not able to involve personal preferences to
exclude out of interest areas. In this paper, we propose a novel hybrid deep
learning architecture for interactive region segmentation and captioning where
the user is able to specify an arbitrary region of the image that should be
processed. To this end, a dedicated Fully Convolutional Network (FCN) named
Lyncean FCN (LFCN) is trained using our special training data to isolate the
User Intention Region (UIR) as the output of an efficient segmentation. In
parallel, a dense image captioning model is utilized to provide a wide variety
of captions for that region. Then, the UIR will be explained with the caption
of the best match bounding box. To the best of our knowledge, this is the first
work that provides such a comprehensive output. Our experiments show the
superiority of the proposed approach over state-of-the-art interactive
segmentation methods on several well-known datasets. In addition, replacement
of the bounding boxes with the result of the interactive segmentation leads to
a better understanding of the dense image captioning output as well as accuracy
enhancement for the object detection in terms of Intersection over Union (IoU).Comment: 17, pages, 9 figure
A Multi-cut Formulation for Joint Segmentation and Tracking of Multiple Objects
Recently, Minimum Cost Multicut Formulations have been proposed and proven to
be successful in both motion trajectory segmentation and multi-target tracking
scenarios. Both tasks benefit from decomposing a graphical model into an
optimal number of connected components based on attractive and repulsive
pairwise terms. The two tasks are formulated on different levels of granularity
and, accordingly, leverage mostly local information for motion segmentation and
mostly high-level information for multi-target tracking. In this paper we argue
that point trajectories and their local relationships can contribute to the
high-level task of multi-target tracking and also argue that high-level cues
from object detection and tracking are helpful to solve motion segmentation. We
propose a joint graphical model for point trajectories and object detections
whose Multicuts are solutions to motion segmentation {\it and} multi-target
tracking problems at once. Results on the FBMS59 motion segmentation benchmark
as well as on pedestrian tracking sequences from the 2D MOT 2015 benchmark
demonstrate the promise of this joint approach
Scatteract: Automated extraction of data from scatter plots
Charts are an excellent way to convey patterns and trends in data, but they
do not facilitate further modeling of the data or close inspection of
individual data points. We present a fully automated system for extracting the
numerical values of data points from images of scatter plots. We use deep
learning techniques to identify the key components of the chart, and optical
character recognition together with robust regression to map from pixels to the
coordinate system of the chart. We focus on scatter plots with linear scales,
which already have several interesting challenges. Previous work has done fully
automatic extraction for other types of charts, but to our knowledge this is
the first approach that is fully automatic for scatter plots. Our method
performs well, achieving successful data extraction on 89% of the plots in our
test set.Comment: Submitted to ECML PKDD 2017 proceedings, 16 page
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