Video anomaly detection and localization via Gaussian Mixture Fully Convolutional Variational Autoencoder

The aim of this paper was the detection of pathologies through respiratory sounds. The ICBHI (International Conference on Biomedical and Health Informatics) Benchmark was used. This dataset is composed of 920 sounds of which 810 are of chronic diseases, 75 of non-chronic diseases and only 35 of healthy individuals. As more than 88% of the samples of the dataset are from the same class (Chronic), the use of a Variational Convolutional Autoencoder was proposed to generate new labeled data and other well known oversampling techniques after determining that the dataset classes are unbalanced. Once the preprocessing step was carried out, a Convolutional Neural Network (CNN) was used to classify the respiratory sounds into healthy, chronic, and non-chronic disease. In addition, we carried out a more challenging classification trying to distinguish between the different types of pathologies or healthy: URTI, COPD, Bronchiectasis, Pneumonia, and Bronchiolitis. We achieved results up to 0.993 F-Score in the three-label classification and 0.990 F-Score in the more challenging six-class classification.

Section: Deep Learning Techniquesmentioning

confidence: 99%

Detecting Respiratory Pathologies Using Convolutional Neural Networks and Variational Autoencoders for Unbalancing Data

García-Ordás

Benítez-Andrades

García-Rodríguez

et al. 2020

“…Furthermore, authors in [28] propose a multi-resolution CNN in the wavelet domain that extracts features independent of phase shifts. Our proposed methodology, instead, leverages a 1D-CNN based Variational AutoEncoder to extract relevant information from the morphology of SCG heartbeats, previously segmented by means of an unsupervised technique; the use of VAE implies a generative model, which may prove useful, e.g., in the context of anomaly detection [29][30][31][32].…”

Section: Related Workmentioning

confidence: 99%

Detection and Analysis of Heartbeats in Seismocardiogram Signals

Mora

Cocconcelli

Matrella

et al. 2020

This paper presents an unsupervised methodology to analyze SeismoCardioGram (SCG) signals. Starting from raw accelerometric data, heartbeat complexes are extracted and annotated, using a two-step procedure. An unsupervised calibration procedure is added to better adapt to different user patterns. Results show that the performance scores achieved by the proposed methodology improve over related literature: on average, 98.5% sensitivity and 98.6% precision are achieved in beat detection, whereas RMS (Root Mean Square) error in heartbeat interval estimation is as low as 4.6 ms. This allows SCG heartbeat complexes to be reliably extracted. Then, the morphological information of such waveforms is further processed by means of a modular Convolutional Variational AutoEncoder network, aiming at extracting compressed, meaningful representation. After unsupervised training, the VAE network is able to recognize different signal morphologies, associating each user to its specific patterns with high accuracy, as indicated by specific performance metrics (including adjusted random and mutual information score, completeness, and homogeneity). Finally, a Linear Model is used to interpret the results of clustering in the learned latent space, highlighting the impact of different VAE architectural parameters (i.e., number of stacked convolutional units and dimension of latent space).

“…In their innovative approach, the authors formulated an information-theoretic active learning technique that utilizes contextual information among activities and objects. Other interesting current examples are presented in References [ 18 , 19 ]. In the first, a novel end-to-end partially supervised deep learning approach for video anomaly detection and localization using only normal samples is presented.…”

Section: Introductionmentioning

confidence: 99%

Bodyprint—A Meta-Feature Based LSTM Hashing Model for Person Re-Identification

Avola

Cinque

Fagioli

et al. 2020

Person re-identification is concerned with matching people across disjointed camera views at different places and different time instants. This task results of great interest in computer vision, especially in video surveillance applications where the re-identification and tracking of persons are required on uncontrolled crowded spaces and after long time periods. The latter aspects are responsible for most of the current unsolved problems of person re-identification, in fact, the presence of many people in a location as well as the passing of hours or days give arise to important visual appearance changes of people, for example, clothes, lighting, and occlusions; thus making person re-identification a very hard task. In this paper, for the first time in the state-of-the-art, a meta-feature based Long Short-Term Memory (LSTM) hashing model for person re-identification is presented. Starting from 2D skeletons extracted from RGB video streams, the proposed method computes a set of novel meta-features based on movement, gait, and bone proportions. These features are analysed by a network composed of a single LSTM layer and two dense layers. The first layer is used to create a pattern of the person’s identity, then, the seconds are used to generate a bodyprint hash through binary coding. The effectiveness of the proposed method is tested on three challenging datasets, that is, iLIDS-VID, PRID 2011, and MARS. In particular, the reported results show that the proposed method, which is not based on visual appearance of people, is fully competitive with respect to other methods based on visual features. In addition, thanks to its skeleton model abstraction, the method results to be a concrete contribute to address open problems, such as long-term re-identification and severe illumination changes, which tend to heavily influence the visual appearance of persons.