35 research outputs found
Object Recognition from very few Training Examples for Enhancing Bicycle Maps
In recent years, data-driven methods have shown great success for extracting
information about the infrastructure in urban areas. These algorithms are
usually trained on large datasets consisting of thousands or millions of
labeled training examples. While large datasets have been published regarding
cars, for cyclists very few labeled data is available although appearance,
point of view, and positioning of even relevant objects differ. Unfortunately,
labeling data is costly and requires a huge amount of work. In this paper, we
thus address the problem of learning with very few labels. The aim is to
recognize particular traffic signs in crowdsourced data to collect information
which is of interest to cyclists. We propose a system for object recognition
that is trained with only 15 examples per class on average. To achieve this, we
combine the advantages of convolutional neural networks and random forests to
learn a patch-wise classifier. In the next step, we map the random forest to a
neural network and transform the classifier to a fully convolutional network.
Thereby, the processing of full images is significantly accelerated and
bounding boxes can be predicted. Finally, we integrate data of the Global
Positioning System (GPS) to localize the predictions on the map. In comparison
to Faster R-CNN and other networks for object recognition or algorithms for
transfer learning, we considerably reduce the required amount of labeled data.
We demonstrate good performance on the recognition of traffic signs for
cyclists as well as their localization in maps.Comment: Submitted to IV 2018. This research was supported by German Research
Foundation DFG within Priority Research Programme 1894 "Volunteered
Geographic Information: Interpretation, Visualization and Social Computing
Uncalibrated Non-Rigid Factorisation by Independent Subspace Analysis
We propose a general, prior-free approach for the uncalibrated non-rigid
structure-from-motion problem for modelling and analysis of non-rigid objects
such as human faces. The word general refers to an approach that recovers the
non-rigid affine structure and motion from 2D point correspondences by assuming
that (1) the non-rigid shapes are generated by a linear combination of rigid 3D
basis shapes, (2) that the non-rigid shapes are affine in nature, i.e., they
can be modelled as deviations from the mean, rigid shape, (3) and that the
basis shapes are statistically independent. In contrast to the majority of
existing works, no prior information is assumed for the structure and motion
apart from the assumption the that underlying basis shapes are statistically
independent. The independent 3D shape bases are recovered by independent
subspace analysis (ISA). Likewise, in contrast to the most previous approaches,
no calibration information is assumed for affine cameras; the reconstruction is
solved up to a global affine ambiguity that makes our approach simple but
efficient. In the experiments, we evaluated the method with several standard
data sets including a real face expression data set of 7200 faces with 2D point
correspondences and unknown 3D structure and motion for which we obtained
promising results
Object Recognition from very few Training Examples for Enhancing Bicycle Maps
In recent years, data-driven methods have shown great success for extracting information about the infrastructure in urban areas. These algorithms are usually trained on large datasets consisting of thousands or millions of labeled training examples. While large datasets have been published regarding cars, for cyclists very few labeled data is available although appearance, point of view, and positioning of even relevant objects differ. Unfortunately, labeling data is costly and requires a huge amount of work. In this paper, we thus address the problem of learning with very few labels. The aim is to recognize particular traffic signs in crowdsourced data to collect information which is of interest to cyclists. We propose a system for object recognition that is trained with only 15 examples per class on average. To achieve this, we combine the advantages of convolutional neural networks and random forests to learn a patch-wise classifier. In the next step, we map the random forest to a neural network and transform the classifier to a fully convolutional network. Thereby, the processing of full images is significantly accelerated and bounding boxes can be predicted. Finally, we integrate data of the Global Positioning System (GPS) to localize the predictions on the map. In comparison to Faster R-CNN and other networks for object recognition or algorithms for transfer learning, we considerably reduce the required amount of labeled data. We demonstrate good performance on the recognition of traffic signs for cyclists as well as their localization in maps
Learning convolutional neural networks for object detection with very little training data
In recent years, convolutional neural networks have shown great success in various computer vision tasks such as classification, object detection, and scene analysis. These algorithms are usually trained on large datasets consisting of thousands or millions of labeled training examples. The availability of sufficient data, however, limits possible applications. While large amounts of data can be quickly collected, supervised learning further requires labeled data. Labeling data, unfortunately, is usually very time-consuming and literally expensive. This chapter addresses the problem of learning with very little labeled data for extracting information about the infrastructure in urban areas. The aim is to recognize particular traffic signs in crowdsourced data to collect information which is of interest to cyclists. The presented system for object detection is trained with very few training examples. To achieve this, the advantages of convolutional neural networks and random forests are combined to learn a patch-wise classifier. In the next step, the random forest is mapped to a neural network and the classifier is transformed to a fully convolutional network. Thereby, the processing of full images is significantly accelerated and bounding boxes can be predicted. Finally, GPS-data is integrated to localize the predictions on the map and multiple observations are merged to further improve the localization accuracy. In comparison to faster R-CNN and other networks for object detection or algorithms for transfer learning, the required amount of labeled data is considerably reduced.</p
Temporally Consistent Horizon Lines
The horizon line is an important geometric feature for many image processing
and scene understanding tasks in computer vision. For instance, in navigation
of autonomous vehicles or driver assistance, it can be used to improve 3D
reconstruction as well as for semantic interpretation of dynamic environments.
While both algorithms and datasets exist for single images, the problem of
horizon line estimation from video sequences has not gained attention. In this
paper, we show how convolutional neural networks are able to utilise the
temporal consistency imposed by video sequences in order to increase the
accuracy and reduce the variance of horizon line estimates. A novel CNN
architecture with an improved residual convolutional LSTM is presented for
temporally consistent horizon line estimation. We propose an adaptive loss
function that ensures stable training as well as accurate results. Furthermore,
we introduce an extension of the KITTI dataset which contains precise horizon
line labels for 43699 images across 72 video sequences. A comprehensive
evaluation shows that the proposed approach consistently achieves superior
performance compared with existing methods
Deconfounded Imitation Learning
Standard imitation learning can fail when the expert demonstrators have
different sensory inputs than the imitating agent. This is because partial
observability gives rise to hidden confounders in the causal graph. We break
down the space of confounded imitation learning problems and identify three
settings with different data requirements in which the correct imitation policy
can be identified. We then introduce an algorithm for deconfounded imitation
learning, which trains an inference model jointly with a latent-conditional
policy. At test time, the agent alternates between updating its belief over the
latent and acting under the belief. We show in theory and practice that this
algorithm converges to the correct interventional policy, solves the
confounding issue, and can under certain assumptions achieve an asymptotically
optimal imitation performance