9,662 research outputs found
Aerial Vehicle Tracking by Adaptive Fusion of Hyperspectral Likelihood Maps
Hyperspectral cameras can provide unique spectral signatures for consistently
distinguishing materials that can be used to solve surveillance tasks. In this
paper, we propose a novel real-time hyperspectral likelihood maps-aided
tracking method (HLT) inspired by an adaptive hyperspectral sensor. A moving
object tracking system generally consists of registration, object detection,
and tracking modules. We focus on the target detection part and remove the
necessity to build any offline classifiers and tune a large amount of
hyperparameters, instead learning a generative target model in an online manner
for hyperspectral channels ranging from visible to infrared wavelengths. The
key idea is that, our adaptive fusion method can combine likelihood maps from
multiple bands of hyperspectral imagery into one single more distinctive
representation increasing the margin between mean value of foreground and
background pixels in the fused map. Experimental results show that the HLT not
only outperforms all established fusion methods but is on par with the current
state-of-the-art hyperspectral target tracking frameworks.Comment: Accepted at the International Conference on Computer Vision and
Pattern Recognition Workshops, 201
Vacancy state detector oriented to convolutional neural network, background subtraction and embedded systems
Dupla diplomação com a UTFPR - Universidade Tecnológica Federal do ParanáMuch has been discussed recently related to population ascension, the reasons for this
event, and, in particular, the aspects of society affected. Over the years, the city governments
realized a higher level of growth, mainly in terms of urban scale, technology, and
individuals numbers. It comprises improvements and investments in their structure and
policies, motivated by improving conditions in population live quality and reduce environmental,
energy, fuel, time, and money resources, besides population living costs, including
the increasing demand for parking structures accessible to the general or private-public,
and a waste of substantial daily time and fuel, disturbing the population routinely. Therefore,
one way to achieve that challenge is focused on reducing energy, money, and time
costs to travel to work or travel to another substantial location. That work presents a
robust, and low computational power Smart Parking system adaptive to several environments
changes to detect and report vacancy states in a parking space oriented to Deep
Learning, and Embedded Systems. This project consists of determining the parking vacancy
status through statistical and image processing methods, creates a robust image
data set, and the Convolutional Neural Network model focused on predict three final
classes. In order to save computational power, this approach uses the Background Subtraction
based on the Mixture of Gaussian method, only updating parking space status,
in which large levels of motion are detected. The proposed model presents 94 percent of
precision at the designed domain.Muito se discutiu recentemente sobre a ascensão populacional, as razões deste evento
e, em particular, os aspectos da sociedade afetados. Ao longo dos anos, os governos
perceberam um grande nível de crescimento, principalmente em termos de escala urbana,
tecnologia e número de indivíduos. Este fato deve-se a melhorias e investimentos na estrutura
urbana e políticas motivados por melhorar as condições de qualidade de vida da
população e reduzir a utilização de recursos ambientais, energéticos, combustíveis, temporais
e monetários, além dos custos de vida da população, incluindo a crescente demanda
por estruturas de estacionamento acessíveis ao público em geral ou público-privado. Portanto,
uma maneira de alcançar esse desafio é manter a atenção na redução de custos de
energia, dinheiro e tempo para viajar para o trabalho ou para outro local substancial.
Esse trabalho apresenta um sistema robusto de Smart Parking, com baixo consumo computacional,
adaptável a diversas mudanças no ambiente observado para detectar e relatar
os estados das vagas de estacionamento, orientado por Deep Learning e Embedded Systems.
Este projeto consiste em determinar o status da vaga de estacionamento por meio
de métodos estatísticos e de processamento de imagem, criando um conjunto robusto de
dados e um modelo de Rede Neuronal Convolucional com foco na previsão de três classes
finais. A fim de reduzir consumo computacional, essa abordagem usa o método de Background
Subtraction, somente atualizando o status do espaço de estacionamento em que
grandes níveis de movimento são detectados. O modelo proposto apresenta 94 porcento
da precisão no domínio projetado
Data analytics for smart parking applications
We consider real-life smart parking systems where parking lot occupancy data are collected from field sensor devices and sent to backend servers for further processing and usage for applications. Our objective is to make these data useful to end users, such as parking managers, and, ultimately, to citizens. To this end, we concoct and validate an automated classification algorithm having two objectives: (1) outlier detection: to detect sensors with anomalous behavioral patterns, i.e., outliers; and (2) clustering: to group the parking sensors exhibiting similar patterns into distinct clusters. We first analyze the statistics of real parking data, obtaining suitable simulation models for parking traces. We then consider a simple classification algorithm based on the empirical complementary distribution function of occupancy times and show its limitations. Hence, we design a more sophisticated algorithm exploiting unsupervised learning techniques (self-organizing maps). These are tuned following a supervised approach using our trace generator and are compared against other clustering schemes, namely expectation maximization, k-means clustering and DBSCAN, considering six months of data from a real sensor deployment. Our approach is found to be superior in terms of classification accuracy, while also being capable of identifying all of the outliers in the dataset
Automatic vacant parking places management system using multicamera vehicle detection
This paper presents a multicamera system for vehicles
detection and their corresponding mapping into the parking
spots of a parking lot. Approaches from the state-of-the-art,
which work properly in controlled scenarios, have been validated
using small amount of sequences and without more challenging
realistic conditions (illumniation changes, different weather). On
the other hand, most of them are not complete systems, but
provide only parts of them, usually detectors. The proposed
system has been designed for realistic scenarios considering
different cases of occlussion, ilumination changes and different
climatic conditions; a real scenario (the International Pittsburgh
Airport parking lot) has been targeted with the condition that
existing parking security cameras can be used, avoiding the
deployment of new cameras or other sensors infrastructures.
For design and validation, a new multicamera dataset has been
recorded. The system is based on existing object detectors (the
results of two of them are shown) and different proposed postprocessing
stages. The results clearly show that the proposed system
works correctly in challenging scenarios including almost total
occlusions, illumination changes and different weather conditionsThis work has been partially supported by the Spanish
Government FPU grant programme (Ministerio de Educación,
Cultura y Deporte) and by the Spanish government under
the project TEC2014-53176-R (HAVideo
- …