1,240 research outputs found
Design, implementation and evaluation of automated surveillance systems
El reconocimiento de patrones ha conseguido un nivel de complejidad que nos permite reconocer diferente
tipo de eventos, incluso peligros, y actuar en concordancia para minimizar el impacto de una situación
complicada y abordarla de la mejor manera posible. Sin embargo, creemos que todavía se puede llegar
a alcanzar aplicaciones más eficientes con algoritmos más precisos. Nuestra aplicación quiere probar
a incluir el nuevo paradigma de la programación, las redes neuronales. Nuestra idea en principio fue
explorar la alternativa que las nuevas redes neuronales convolucionales aportaban, en donde se podía
ver en vídeos de ejemplos la alta tasa de detección e identificación que, por ejemplo, YOLOv2 podría
mostrar. Después de comparar las características, vimos que YOLOv3 ofrecía un buen balance entre
precisión y rapidez como comentaremos más adelante. Debido a la tasa de baja detecciones, haremos
uso de los filtros de Kalman para ayudarnos a la hora de hacer reidentificación de personas y objetos.
En este proyecto, haremos un estudio además de las alternativas de videovigilancia con las que cuentan
empresas del sector y veremos que clase de productos ofrecen y, por otro lado, observaremos cuales son
los trabajos de los grupos de investigadores de otras universidades que más similitudes tienen con nuestro objetivo. Dedicaremos, por lo tanto, el uso de esta red neuronal para detectar eventos como el abandono de mochilas y para mostrar la densidad de tránsito en localizaciones concretas, así como utilizaremos una metodología más tradicional, el flujo óptico, para detectar actuaciones anormales en una multitud.Automatic surveillance system is getting more and more sophisticated with the increasing calculation
power that computers are reaching. The aim of this project is to take advantage of these tools and
with the new classification and detection technology brought by neural networks, develop a surveillance
application that can recognize certain behaviours (which are the detection of lost backpacks and suitcases,
detection of abnormal crowd activity and heatmap of density occupation). To develop this program,
python has been the selected programming language used, where YOLO and OpenCV form the spine of
this project. After testing the code, it has been proved that due to the constrains of the detection for
small objects, the project does not perform as it should for real development, but still it shows potential
for the detection of lost backpacks in certain videos from the GBA dataset [1] and PETS2006 dataset [2].
The abnormal activity detection for crowds is made with a simple algorithm that seems to perform well,
detecting the anomalies in all the testing dataset used, generated by the University of Minnesota [3].
Finally, the heatmap can display correctly the projection of people on the ground for five second, just as
intended. The objective of this software is to be part of the core of what could be a future application
with more modules that will be able to perform full automated surveillance tasks and gather useful
information data, and these advances and future proposal will be explained in this memory.Máster Universitario en Ingeniería Industrial (M141
SigSegment: A Signal-Based Segmentation Algorithm for Identifying Anomalous Driving Behaviours in Naturalistic Driving Videos
In recent years, distracted driving has garnered considerable attention as it
continues to pose a significant threat to public safety on the roads. This has
increased the need for innovative solutions that can identify and eliminate
distracted driving behavior before it results in fatal accidents. In this
paper, we propose a Signal-Based anomaly detection algorithm that segments
videos into anomalies and non-anomalies using a deep CNN-LSTM classifier to
precisely estimate the start and end times of an anomalous driving event. In
the phase of anomaly detection and analysis, driver pose background estimation,
mask extraction, and signal activity spikes are utilized. A Deep CNN-LSTM
classifier was applied to candidate anomalies to detect and classify final
anomalies. The proposed method achieved an overlap score of 0.5424 and ranked
9th on the public leader board in the AI City Challenge 2023, according to
experimental validation results
Automatic Assessment of the Type and Intensity of Agitated Hand Movements
With increasing numbers of people living with dementia, there is growing interest in the automatic monitoring of agitation. Current assessments rely on carer observations within a framework of behavioural scales. Automatic monitoring of agitation can supplement existing assessments, providing carers and clinicians with a greater understanding of the causes and extent of agitation. Despite agitation frequently manifesting in repetitive hand movements, the automatic assessment of repetitive hand movements remains a sparsely researched field. Monitoring hand movements is problematic due to the subtle differences between different types of hand movements and variations in how they can be carried out; the lack of training data creates additional challenges. This paper proposes a novel approach to assess the type and intensity of repetitive hand movements using skeletal model data derived from video. We introduce a video-based dataset of five repetitive hand movements symptomatic of agitation. Using skeletal keypoint locations extracted from video, we demonstrate a system to recognise repetitive hand movements using discriminative poses. By first learning characteristics of the movement, our system can accurately identify changes in the intensity of repetitive movements. Wide inter-subject variation in agitated behaviours suggests the benefit of personalising the recognition model with some end-user information. Our results suggest that data captured using a single conventional RGB video camera can be used to automatically monitor agitated hand movements of sedentary patients
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