4,288 research outputs found
Radar-based Feature Design and Multiclass Classification for Road User Recognition
The classification of individual traffic participants is a complex task,
especially for challenging scenarios with multiple road users or under bad
weather conditions. Radar sensors provide an - with respect to well established
camera systems - orthogonal way of measuring such scenes. In order to gain
accurate classification results, 50 different features are extracted from the
measurement data and tested on their performance. From these features a
suitable subset is chosen and passed to random forest and long short-term
memory (LSTM) classifiers to obtain class predictions for the radar input.
Moreover, it is shown why data imbalance is an inherent problem in automotive
radar classification when the dataset is not sufficiently large. To overcome
this issue, classifier binarization is used among other techniques in order to
better account for underrepresented classes. A new method to couple the
resulting probabilities is proposed and compared to others with great success.
Final results show substantial improvements when compared to ordinary
multiclass classificationComment: 8 pages, 6 figure
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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
Fully Convolutional Neural Networks for Dynamic Object Detection in Grid Maps
Grid maps are widely used in robotics to represent obstacles in the
environment and differentiating dynamic objects from static infrastructure is
essential for many practical applications. In this work, we present a methods
that uses a deep convolutional neural network (CNN) to infer whether grid cells
are covering a moving object or not. Compared to tracking approaches, that use
e.g. a particle filter to estimate grid cell velocities and then make a
decision for individual grid cells based on this estimate, our approach uses
the entire grid map as input image for a CNN that inspects a larger area around
each cell and thus takes the structural appearance in the grid map into account
to make a decision. Compared to our reference method, our concept yields a
performance increase from 83.9% to 97.2%. A runtime optimized version of our
approach yields similar improvements with an execution time of just 10
milliseconds.Comment: This is a shorter version of the masters thesis of Florian Piewak and
it was accapted at IV 201
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