814 research outputs found
Combining LiDAR Space Clustering and Convolutional Neural Networks for Pedestrian Detection
Pedestrian detection is an important component for safety of autonomous
vehicles, as well as for traffic and street surveillance. There are extensive
benchmarks on this topic and it has been shown to be a challenging problem when
applied on real use-case scenarios. In purely image-based pedestrian detection
approaches, the state-of-the-art results have been achieved with convolutional
neural networks (CNN) and surprisingly few detection frameworks have been built
upon multi-cue approaches. In this work, we develop a new pedestrian detector
for autonomous vehicles that exploits LiDAR data, in addition to visual
information. In the proposed approach, LiDAR data is utilized to generate
region proposals by processing the three dimensional point cloud that it
provides. These candidate regions are then further processed by a
state-of-the-art CNN classifier that we have fine-tuned for pedestrian
detection. We have extensively evaluated the proposed detection process on the
KITTI dataset. The experimental results show that the proposed LiDAR space
clustering approach provides a very efficient way of generating region
proposals leading to higher recall rates and fewer misses for pedestrian
detection. This indicates that LiDAR data can provide auxiliary information for
CNN-based approaches
Optimality in multiple comparison procedures
When many (m) null hypotheses are tested with a single dataset, the control
of the number of false rejections is often the principal consideration. Two
popular controlling rates are the probability of making at least one false
discovery (FWER) and the expected fraction of false discoveries among all
rejections (FDR). Scaled multiple comparison error rates form a new family that
bridges the gap between these two extremes. For example, the Scaled Expected
Value (SEV) limits the number of false positives relative to an arbitrary
increasing function of the number of rejections, that is, E(FP/s(R)). We
discuss the problem of how to choose in practice which procedure to use, with
elements of an optimality theory, by considering the number of false rejections
FP separately from the number of correct rejections TP. Using this framework we
will show how to choose an element in the new family mentioned above.Comment: arXiv admin note: text overlap with arXiv:1112.451
a combined top-down and bottom-up approach
The thesis focuses on the interoperability of autonomous legacy databases with
the idea of meeting the actual requirements of an organization. The
interoperability is resolved by combining the topdown and bottom-up
strategies. The legacy objects are extracted from the existing databases
through a database reverse engineering process. The business objects are
defined by both the organization requirements and the integration of the
legacy objects
Using Photorealistic Face Synthesis and Domain Adaptation to Improve Facial Expression Analysis
Cross-domain synthesizing realistic faces to learn deep models has attracted
increasing attention for facial expression analysis as it helps to improve the
performance of expression recognition accuracy despite having small number of
real training images. However, learning from synthetic face images can be
problematic due to the distribution discrepancy between low-quality synthetic
images and real face images and may not achieve the desired performance when
the learned model applies to real world scenarios. To this end, we propose a
new attribute guided face image synthesis to perform a translation between
multiple image domains using a single model. In addition, we adopt the proposed
model to learn from synthetic faces by matching the feature distributions
between different domains while preserving each domain's characteristics. We
evaluate the effectiveness of the proposed approach on several face datasets on
generating realistic face images. We demonstrate that the expression
recognition performance can be enhanced by benefiting from our face synthesis
model. Moreover, we also conduct experiments on a near-infrared dataset
containing facial expression videos of drivers to assess the performance using
in-the-wild data for driver emotion recognition.Comment: 8 pages, 8 figures, 5 tables, accepted by FG 2019. arXiv admin note:
substantial text overlap with arXiv:1905.0028
Combining Multiple Views for Visual Speech Recognition
Visual speech recognition is a challenging research problem with a particular
practical application of aiding audio speech recognition in noisy scenarios.
Multiple camera setups can be beneficial for the visual speech recognition
systems in terms of improved performance and robustness. In this paper, we
explore this aspect and provide a comprehensive study on combining multiple
views for visual speech recognition. The thorough analysis covers fusion of all
possible view angle combinations both at feature level and decision level. The
employed visual speech recognition system in this study extracts features
through a PCA-based convolutional neural network, followed by an LSTM network.
Finally, these features are processed in a tandem system, being fed into a
GMM-HMM scheme. The decision fusion acts after this point by combining the
Viterbi path log-likelihoods. The results show that the complementary
information contained in recordings from different view angles improves the
results significantly. For example, the sentence correctness on the test set is
increased from 76% for the highest performing single view () to up to
83% when combining this view with the frontal and view angles
Learn to synthesize and synthesize to learn
Attribute guided face image synthesis aims to manipulate attributes on a face
image. Most existing methods for image-to-image translation can either perform
a fixed translation between any two image domains using a single attribute or
require training data with the attributes of interest for each subject.
Therefore, these methods could only train one specific model for each pair of
image domains, which limits their ability in dealing with more than two
domains. Another disadvantage of these methods is that they often suffer from
the common problem of mode collapse that degrades the quality of the generated
images. To overcome these shortcomings, we propose attribute guided face image
generation method using a single model, which is capable to synthesize multiple
photo-realistic face images conditioned on the attributes of interest. In
addition, we adopt the proposed model to increase the realism of the simulated
face images while preserving the face characteristics. Compared to existing
models, synthetic face images generated by our method present a good
photorealistic quality on several face datasets. Finally, we demonstrate that
generated facial images can be used for synthetic data augmentation, and
improve the performance of the classifier used for facial expression
recognition.Comment: Accepted to Computer Vision and Image Understanding (CVIU
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