2,613 research outputs found
Group-level Emotion Recognition using Transfer Learning from Face Identification
In this paper, we describe our algorithmic approach, which was used for
submissions in the fifth Emotion Recognition in the Wild (EmotiW 2017)
group-level emotion recognition sub-challenge. We extracted feature vectors of
detected faces using the Convolutional Neural Network trained for face
identification task, rather than traditional pre-training on emotion
recognition problems. In the final pipeline an ensemble of Random Forest
classifiers was learned to predict emotion score using available training set.
In case when the faces have not been detected, one member of our ensemble
extracts features from the whole image. During our experimental study, the
proposed approach showed the lowest error rate when compared to other explored
techniques. In particular, we achieved 75.4% accuracy on the validation data,
which is 20% higher than the handcrafted feature-based baseline. The source
code using Keras framework is publicly available.Comment: 5 pages, 3 figures, accepted for publication at ICMI17 (EmotiW Grand
Challenge
Unsupervised Domain Adaptation for Multispectral Pedestrian Detection
Multimodal information (e.g., visible and thermal) can generate robust
pedestrian detections to facilitate around-the-clock computer vision
applications, such as autonomous driving and video surveillance. However, it
still remains a crucial challenge to train a reliable detector working well in
different multispectral pedestrian datasets without manual annotations. In this
paper, we propose a novel unsupervised domain adaptation framework for
multispectral pedestrian detection, by iteratively generating pseudo
annotations and updating the parameters of our designed multispectral
pedestrian detector on target domain. Pseudo annotations are generated using
the detector trained on source domain, and then updated by fixing the
parameters of detector and minimizing the cross entropy loss without
back-propagation. Training labels are generated using the pseudo annotations by
considering the characteristics of similarity and complementarity between
well-aligned visible and infrared image pairs. The parameters of detector are
updated using the generated labels by minimizing our defined multi-detection
loss function with back-propagation. The optimal parameters of detector can be
obtained after iteratively updating the pseudo annotations and parameters.
Experimental results show that our proposed unsupervised multimodal domain
adaptation method achieves significantly higher detection performance than the
approach without domain adaptation, and is competitive with the supervised
multispectral pedestrian detectors
Deep Detection of People and their Mobility Aids for a Hospital Robot
Robots operating in populated environments encounter many different types of
people, some of whom might have an advanced need for cautious interaction,
because of physical impairments or their advanced age. Robots therefore need to
recognize such advanced demands to provide appropriate assistance, guidance or
other forms of support. In this paper, we propose a depth-based perception
pipeline that estimates the position and velocity of people in the environment
and categorizes them according to the mobility aids they use: pedestrian,
person in wheelchair, person in a wheelchair with a person pushing them, person
with crutches and person using a walker. We present a fast region proposal
method that feeds a Region-based Convolutional Network (Fast R-CNN). With this,
we speed up the object detection process by a factor of seven compared to a
dense sliding window approach. We furthermore propose a probabilistic position,
velocity and class estimator to smooth the CNN's detections and account for
occlusions and misclassifications. In addition, we introduce a new hospital
dataset with over 17,000 annotated RGB-D images. Extensive experiments confirm
that our pipeline successfully keeps track of people and their mobility aids,
even in challenging situations with multiple people from different categories
and frequent occlusions. Videos of our experiments and the dataset are
available at http://www2.informatik.uni-freiburg.de/~kollmitz/MobilityAidsComment: 7 pages, ECMR 2017, dataset and videos:
http://www2.informatik.uni-freiburg.de/~kollmitz/MobilityAids
Emotion Recognition in the Wild using Deep Neural Networks and Bayesian Classifiers
Group emotion recognition in the wild is a challenging problem, due to the
unstructured environments in which everyday life pictures are taken. Some of
the obstacles for an effective classification are occlusions, variable lighting
conditions, and image quality. In this work we present a solution based on a
novel combination of deep neural networks and Bayesian classifiers. The neural
network works on a bottom-up approach, analyzing emotions expressed by isolated
faces. The Bayesian classifier estimates a global emotion integrating top-down
features obtained through a scene descriptor. In order to validate the system
we tested the framework on the dataset released for the Emotion Recognition in
the Wild Challenge 2017. Our method achieved an accuracy of 64.68% on the test
set, significantly outperforming the 53.62% competition baseline.Comment: accepted by the Fifth Emotion Recognition in the Wild (EmotiW)
Challenge 201
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