86,497 research outputs found
Abnormal Event Detection in Videos using Spatiotemporal Autoencoder
We present an efficient method for detecting anomalies in videos. Recent
applications of convolutional neural networks have shown promises of
convolutional layers for object detection and recognition, especially in
images. However, convolutional neural networks are supervised and require
labels as learning signals. We propose a spatiotemporal architecture for
anomaly detection in videos including crowded scenes. Our architecture includes
two main components, one for spatial feature representation, and one for
learning the temporal evolution of the spatial features. Experimental results
on Avenue, Subway and UCSD benchmarks confirm that the detection accuracy of
our method is comparable to state-of-the-art methods at a considerable speed of
up to 140 fps
EDEN: Evolutionary Deep Networks for Efficient Machine Learning
Deep neural networks continue to show improved performance with increasing
depth, an encouraging trend that implies an explosion in the possible
permutations of network architectures and hyperparameters for which there is
little intuitive guidance. To address this increasing complexity, we propose
Evolutionary DEep Networks (EDEN), a computationally efficient
neuro-evolutionary algorithm which interfaces to any deep neural network
platform, such as TensorFlow. We show that EDEN evolves simple yet successful
architectures built from embedding, 1D and 2D convolutional, max pooling and
fully connected layers along with their hyperparameters. Evaluation of EDEN
across seven image and sentiment classification datasets shows that it reliably
finds good networks -- and in three cases achieves state-of-the-art results --
even on a single GPU, in just 6-24 hours. Our study provides a first attempt at
applying neuro-evolution to the creation of 1D convolutional networks for
sentiment analysis including the optimisation of the embedding layer.Comment: 7 pages, 3 figures, 3 tables and see video
https://vimeo.com/23451009
An original framework for understanding human actions and body language by using deep neural networks
The evolution of both fields of Computer Vision (CV) and Artificial Neural Networks (ANNs) has allowed the development of efficient automatic systems for the analysis of people's behaviour.
By studying hand movements it is possible to recognize gestures, often used by people to communicate information in a non-verbal way.
These gestures can also be used to control or interact with devices without physically touching them. In particular, sign language and semaphoric hand gestures are the two foremost areas of interest due to their importance in Human-Human Communication (HHC) and Human-Computer Interaction (HCI), respectively.
While the processing of body movements play a key role in the action recognition and affective computing fields. The former is essential to understand how people act in an environment, while the latter tries to interpret people's emotions based on their poses and movements;
both are essential tasks in many computer vision applications, including event recognition, and video surveillance.
In this Ph.D. thesis, an original framework for understanding Actions and body language is presented. The framework is composed of three main modules: in the first one, a Long Short Term Memory Recurrent Neural Networks (LSTM-RNNs) based method for the Recognition of Sign Language and Semaphoric Hand Gestures is proposed; the second module presents a solution based on 2D skeleton and two-branch stacked LSTM-RNNs for action recognition in video sequences; finally, in the last module, a solution for basic non-acted emotion recognition by using 3D skeleton and Deep Neural Networks (DNNs) is provided.
The performances of RNN-LSTMs are explored in depth, due to their ability to model the long term contextual information of temporal sequences, making them suitable for analysing body movements.
All the modules were tested by using challenging datasets, well known in the state of the art, showing remarkable results compared to the current literature methods
Magnetic Resonance Fingerprinting using Recurrent Neural Networks
Magnetic Resonance Fingerprinting (MRF) is a new approach to quantitative
magnetic resonance imaging that allows simultaneous measurement of multiple
tissue properties in a single, time-efficient acquisition. Standard MRF
reconstructs parametric maps using dictionary matching and lacks scalability
due to computational inefficiency. We propose to perform MRF map reconstruction
using a recurrent neural network, which exploits the time-dependent information
of the MRF signal evolution. We evaluate our method on multiparametric
synthetic signals and compare it to existing MRF map reconstruction approaches,
including those based on neural networks. Our method achieves state-of-the-art
estimates of T1 and T2 values. In addition, the reconstruction time is
significantly reduced compared to dictionary-matching based approaches.Comment: Accepted for ISBI 201
Visual Weather Temperature Prediction
In this paper, we attempt to employ convolutional recurrent neural networks
for weather temperature estimation using only image data. We study ambient
temperature estimation based on deep neural networks in two scenarios a)
estimating temperature of a single outdoor image, and b) predicting temperature
of the last image in an image sequence. In the first scenario, visual features
are extracted by a convolutional neural network trained on a large-scale image
dataset. We demonstrate that promising performance can be obtained, and analyze
how volume of training data influences performance. In the second scenario, we
consider the temporal evolution of visual appearance, and construct a recurrent
neural network to predict the temperature of the last image in a given image
sequence. We obtain better prediction accuracy compared to the state-of-the-art
models. Further, we investigate how performance varies when information is
extracted from different scene regions, and when images are captured in
different daytime hours. Our approach further reinforces the idea of using only
visual information for cost efficient weather prediction in the future.Comment: 8 pages, accepted to WACV 201
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