Arquitecturas Flexibles, Crecientes y Jerárquicas para Sistemas Neuronales Autoorganizados

Conferencia impartida por el profesor Esteban José Palomo Ferrer.La autoorganización es un proceso de aprendizaje no supervisado mediante el cual se descubren características, relaciones, patrones significativos o prototipos en los datos. Entre los sistemas neuronales autoorganizados más usados destaca el el mapa autoorganizado o SOM (Self-Organizing Map), el cual ha sido aplicado en multitud de campos distintos. Sin embargo, este modelo autoorganizado tiene varias limitaciones relacionadas con su tamaño, topología, falta de representación de relaciones jerárquicas, etc. La red neuronal llamada gas neuronal creciente o GNG (Growing Neural Gas), es un ejemplo de modelo neuronal autoorganizado con mayor flexibilidad que el SOM ya que está basado en un grafo de unidades de proceso en vez de en una topología fija. A pesar de su éxito, se ha prestado poca atención a su extensión jerárquica, a diferencia de muchos otros modelos que tienen varias versiones jerárquicas. El gas neuronal jerárquico creciente o GHNG (Growing Hierarchical Neural Gas) es una extensión jerárquica del GNG en el que se aprende un árbol de grafos, donde el algoritmo original del GNG se ha mejorado distinguiendo entre una fase de crecimiento y una fase de convergencia. Los resultados experimentales demuestran las capacidades de autoorganización y aprendizaje jerárquico de esta red.Universidad de Málaga. Campus de Excelencia Internacional Andalucía Tech

Pixel Features for Self-organizing Map Based Detection of Foreground Objects in Dynamic Environments

Among current foreground detection algorithms for video sequences, methods based on self-organizing maps are obtaining a greater relevance. In this work we propose a probabilistic self-organising map based model, which uses a uniform distribution to represent the foreground. A suitable set of characteristic pixel features is chosen to train the probabilistic model. Our approach has been compared to some competing methods on a test set of benchmark videos, with favorable results.Universidad de Málaga. Campus de Excelencia Internacional Andalucía Tech

The forbidden region self-organizing map neural network

Self-organizing maps are aimed to learn a representation of the input distribution which faithfully describes the topological relations among the clusters of the distribution. For some datasets and applications, it is known beforehand that some regions of the input space cannot contain any samples. Those are known as forbidden regions. In these cases, any prototype which lies in a forbidden region is meaningless. However, previous self-organizing models do not address this problem. In this work we propose a new self-organizing map model which is guaranteed to keep all prototypes out of a set of prespecified forbidden regions. Experimental results are reported, which show that our proposal outperforms the SOM both in terms of vector quantization error and quality of the learned topological maps

Deep learning-based video surveillance system managed by low cost hardware and panoramic cameras

The design of automated video surveillance systems often involves the detection of agents which exhibit anomalous or dangerous behavior in the scene under analysis. Models aimed to enhance the video pattern recognition abilities of the system are commonly integrated in order to increase its performance. Deep learning neural networks are found among the most popular models employed for this purpose. Nevertheless, the large computational demands of deep networks mean that exhaustive scans of the full video frame make the system perform rather poorly in terms of execution speed when implemented on low cost devices, due to the excessive computational load generated by the examination of multiple image windows. This work presents a video surveillance system aimed to detect moving objects with abnormal behavior for a panoramic 360°surveillance camera. The block of the video frame to be analyzed is determined on the basis of a probabilistic mixture distribution comprised by two mixture components. The first component is a uniform distribution, which is in charge of a blind window selection, while the second component is a mixture of kernel distributions. The kernel distributions generate windows within the video frame in the vicinity of the areas where anomalies were previously found. This contributes to obtain candidate windows for analysis which are close to the most relevant regions of the video frame, according to the past recorded activity. A Raspberry Pi microcontroller based board is employed to implement the system. This enables the design and implementation of a system with a low cost, which is nevertheless capable of performing the video analysis with a high video frame processing rate

Continuous Chemical Classification in Uncontrolled Environments with Sliding Windows.

Electronic noses are sensing devices able to classify chemical volatiles according to the readings of an array of non-selective gas sensors and some pattern recognition algorithm. Given their high versatility to host multiple sensors while still being compact and lightweight, e-noses have demonstrated to be a promising technology to real-world chemical recognition, which is our main concern in this work. Under these scenarios, classification is usually carried out on sub-sequences of the main e-nose data stream after a segmentation phase which objective is to exploit the temporal correlation of the e-nose’s data. In this work we analyze to which extent considering segments of delayed samples by means of fixed-length sliding windows improves the classification accuracy. Extensive experimentation over a variety of experimental scenarios and gas sensor types, together with the analysis of the classification accuracy of three state-of-the-art classifiers, support our conclusions and findings. In particular, it has been found that fixed-length sliding windows attain better results than instantaneous sensor values for several classifier models, with a high statistical significance

Deep learning for coronary artery disease severity classification

Medical imaging evaluations are one of the fields where computed-aid diagnosis could improve the efficiency of diagnosis supporting physician decisions. Cardiovascular Artery Disease (CAD) is diagnosed using the gold standard, Invasive Coronary Angiography (ICA). In this work, performance analysis for binary classification of ICA images considering the severity ranges separately is reported, evaluating how performance is affected depending on the degree of lesions considered. For this purpose, an annotated dataset of ICA images was employed, which contains the ground truth, the location and the category of lesions into seven possible ranges: <20 %, [20 %, 49 %], [50 %, 69 %], [70 %, 89 %], [90 %, 98 %], 99 %, and 100 %. The ICA images were pre-processed, divided into patches and balanced by downsampling and data augmentation. In this study, four known pre-trained CNN architectures were trained using different categories of lesion degree as input, whose F-measures are computed. Results report that the F-measures showed a behavior dependent on the narrow presents of the image, being lesions with more than 50 % severity were better classified, achieving an F-measure of 75%.Universidad de Málaga. Campus de Excelencia Internacional Andalucía Tech

Smart motion detection sensor based on video processing using self-organizing maps

Most current approaches to computer vision are based on expensive, high performance hardware to meet the heavy computational requirements of the employed algorithms. These system architectures are severely limited in their practical application due to financial and technical limitations. In this work a different strategy is used, namely the development of an inexpensive and easy to deploy computer vision system for motion detection. This is achieved by three means. First of all, an affordable and flexible hardware platform is employed. Secondly, the motion detection algorithm is specifically tailored to involve a very small computational load. Thirdly, a fixed point programming paradigm is followed in implementing the system so as to further reduce the computational requirements. The proposed system is experimentally compared to the standard motion detector for a wide range of benchmark videos. The reported results indicate that our proposal attains substantially better performance, while it remains affordable and easy to install in practice

Deep learning-based anomalous object detection system for panoramic cameras managed by a Jetson TX2 board

Social conflicts appearing in the media are increas ing public awareness about security issues, resulting in a higher demand of more exhaustive environment monitoring methods. Automatic video surveillance systems are a powerful assistance to public and private security agents. Since the arrival of deep learn ing, object detection and classification systems have experienced a large improvement in both accuracy and versatility. However, deep learning-based object detection and classification systems often require expensive GPU-based hardware to work properly. This paper presents a novel deep learning-based foreground anomalous object detection system for video streams supplied by panoramic cameras, specially designed to build power efficient video surveillance systems. The system optimises the process of searching for anomalous objects through a new potential detection generator managed by three different multivariant homoscedastic distributions. Experimental results obtained after its deployment in a Jetson TX2 board attest the good performance of the system, postulating it as a solvent approach to power saving video surveillance systems.This work is partially supported by the Ministry of Economy and Competitiveness of Spain under grants TIN2016-75097- P and PPIT.UMA.B1.2017. It is also partially supported by the Ministry of Science, Innovation and Universities of Spain under grant RTI2018-094645-B-I00, project name Automated detection with low-cost hardware of unusual activities in video sequences. It is also partially supported by the Autonomous Government of Andalusia (Spain) under project UMA18- FEDERJA-084, project name Detection of anomalous behavior agents by deep learning in low-cost video surveillance intel ligent systems. All of them include funds from the European Regional Development Fund (ERDF). The authors thankfully acknowledge the computer resources, technical expertise and assistance provided by the SCBI (Supercomputing and Bioin formatics) center of the University of Malaga. They also ´ Authorized licensed use limited to: Universidad de Malaga. Downloaded on February 06,2024 at 07:21:43 UTC from IEEE Xplore. Restrictions apply. gratefully acknowledge the support of NVIDIA Corporation with the donation of two Titan X GPUs used for this research. The authors acknowledge the funding from the Universidad de Malaga

Parallel proccessing applied to object detection with a Jetson TX2 embedded system.

Video streams from panoramic cameras represent a powerful tool for automated surveillance systems, but naïve implementations typically require very intensive computational loads for applying deep learning models for automated detection and tracking of objects of interest, since these models require relatively high resolution to reliably perform object detection. In this paper, we report a host of improvements to our previous state-of-the-art software system to reliably detect and track objects in video streams from panoramic cameras, resulting in an increase in the processing framerate in a Jetson TX2 board, with respect to our previous results. Depending on the number of processes and the load profile, we observe up to a five-fold increase in the framerate.Universidad de Málaga. Campus de Excelencia Internacional Andalucía Tech

A novel continual learning approach for competitive neural networks

Continual learning tries to address the stability-plasticity dilemma to avoid catastrophic forgetting when dealing with non-stationary distributions. Prior works focused on supervised or reinforcement learning, but few works have considered continual learning for unsupervised learning methods. In this paper, a novel approach to provide continual learning for competitive neural networks is proposed. To this end, we have proposed a different learning rate function that can cope with non-stationary distributions by adapting the model to learn continuously. Experimental results performed with different synthetic images that change over time confirm the performance of our proposal.Universidad de Málaga. Campus de Excelencia Internacional Andalucía Tec