54 research outputs found
Bounding Box-Free Instance Segmentation Using Semi-Supervised Learning for Generating a City-Scale Vehicle Dataset
Vehicle classification is a hot computer vision topic, with studies ranging
from ground-view up to top-view imagery. In remote sensing, the usage of
top-view images allows for understanding city patterns, vehicle concentration,
traffic management, and others. However, there are some difficulties when
aiming for pixel-wise classification: (a) most vehicle classification studies
use object detection methods, and most publicly available datasets are designed
for this task, (b) creating instance segmentation datasets is laborious, and
(c) traditional instance segmentation methods underperform on this task since
the objects are small. Thus, the present research objectives are: (1) propose a
novel semi-supervised iterative learning approach using GIS software, (2)
propose a box-free instance segmentation approach, and (3) provide a city-scale
vehicle dataset. The iterative learning procedure considered: (1) label a small
number of vehicles, (2) train on those samples, (3) use the model to classify
the entire image, (4) convert the image prediction into a polygon shapefile,
(5) correct some areas with errors and include them in the training data, and
(6) repeat until results are satisfactory. To separate instances, we considered
vehicle interior and vehicle borders, and the DL model was the U-net with the
Efficient-net-B7 backbone. When removing the borders, the vehicle interior
becomes isolated, allowing for unique object identification. To recover the
deleted 1-pixel borders, we proposed a simple method to expand each prediction.
The results show better pixel-wise metrics when compared to the Mask-RCNN (82%
against 67% in IoU). On per-object analysis, the overall accuracy, precision,
and recall were greater than 90%. This pipeline applies to any remote sensing
target, being very efficient for segmentation and generating datasets.Comment: 38 pages, 10 figures, submitted to journa
Motorcycles detection using Haar-like features and Support Vector Machine on CCTV camera image
Traffic monitoring system allows operators to monitor and analyze each traffic point via CCTV camera. However, it is difficult to monitor each traffic point all the time. This problem leads to the development of intelligent traffic monitoring system using computer vision technology which one of the features is vehicle detection. Vehicle detection still poses a challenge especially when dealing with motorcycles that occupy the majority of the road in Indonesia. In this research, a motorcycle detection method using Haar-like features and Support Vector Machine (SVM) on CCTV camera image is proposed. A set of preprocessing procedure is performed on the input image before Haar-like features extraction. The features then classified using trained SVM model via sliding window technique to detect motorcycles. The test result shows 0.0 log average miss rate and 0.9 average precision. From the low miss rate and high precision, the proposed method shows promising solution in detecting motorcycle from CCTV camera image
DETEKSI KENDARAAN MENGGUNAKAN HISTOGRAM OF ORIENTED GRADIENTS DAN REAL ADABOOST
Sistem deteksi kendaraan merupakan salah satu teknologi yang sangat penting karena memiliki banyak aplikasi dalam bidang lalu lintas seperti pemantauan lalu lintas, penghitungan jumlah kendaraan yang lewat, penghitungan kecepatan kendaraan yang melaju, dan lain sebagainya. Histogram Of Oriented Gradients (HOG) adalah descriptor fitur yang digunakan untuk deteksi objek. HOG mendeskripsikan fitur berdasarkan histogram lokal dari orientasi gradien yang diberi bobot dengan magnitude gradien. Real AdaBoost adalah algoritma learning yang mengkombinasikan weak classifier menjadi strong classifier yang merepresentasikan output akhir dari classifier yang didorong. Penelitian ini bertujuan untuk mendeteksi kendaraan pada citra statis dengan menggunakan metode Histogram Of Oriented Gradients (HOG) dan Real Adaboost. Tahapan dari deteksi kendaraan ini yaitu pra-pemrosessan, proses ekstraksi fitur dengan HOG dan proses klasifikasi dengan Real Adaboost. Dari 257 citra kendaraan dan 35 citra bukan kendaraan didapatkan hasil pengujian dengan tingkat akurasi sebesar 91.78 %
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Vehicle detection system is an important technology because it
has many applications in traffic field such as traffic monitoring,
counting the number of vehicles passing, calculating the speed of
an oncoming vehicle, and so on. Histogram Of Oriented
Gradients (HOG) is a feature descriptor used for the purpose of
object detection. HOG describes features based on local
histogram of gradient orientation weighted by gradient
magnitude. Real AdaBoost is a learning algorithm which
combined weak classifier into strong classifier that represents the
final output of the boosted classifier. This research aims to detect
vehicles in static image using Histogram Of Oriented Gradients
(HOG) methods and Real Adaboost. The steps of vehicle
detection are pra-processing, feature extraction process with
HOG and classification process with Real AdaBoost. The testing
result shows that the system can detect vehicles with an accuracy
level of 91.78 % from 292 testing image, from 257 image of
vehicle and 35 image of not vehicle
Vehicle pose estimation for vehicle detection and tracking based on road direction
Vehicle has several types and each of them has different color, size, and shape. The appearance of vehicle also changes if viewed from different viewpoint of traffic surveillance camera. This situation can create many possibilities of vehicle poses. However, the one in common, vehicle pose usually follows road direction. Therefore, this research proposes a method to estimate the pose of vehicle for vehicle detection and tracking based on road direction. Vehicle training data are generated from 3D vehicle models in four-pair orientation categories. Histogram of Oriented Gradients (HOG) and Linear-Support Vector Machine (Linear-SVM) are used to build vehicle detectors from the data. Road area is extracted from traffic surveillance image to localize the detection area. The pose of vehicle which estimated based on road direction will be used to select a suitable vehicle detector for vehicle detection process. To obtain the final vehicle object, vehicle line checking method is applied to the vehicle detection result. Finally, vehicle tracking is performed to give label on each vehicle. The test conducted on various viewpoints of traffic surveillance camera shows that the method effectively detects and tracks vehicle by estimating the pose of vehicle. Performance evaluation of the proposed method shows 0.9170 of accuracy and 0.9161 of balance accuracy (BAC)
Rich probabilistic models for semantic labeling
Das Ziel dieser Monographie ist es die Methoden und Anwendungen des semantischen Labelings zu erforschen. Unsere Beiträge zu diesem sich rasch entwickelten Thema sind bestimmte Aspekte der Modellierung und der Inferenz in probabilistischen Modellen und ihre Anwendungen in den interdisziplinären Bereichen der Computer Vision sowie medizinischer Bildverarbeitung und Fernerkundung
QUIS-CAMPI: Biometric Recognition in Surveillance Scenarios
The concerns about individuals security have justified the increasing number of surveillance
cameras deployed both in private and public spaces. However, contrary to popular belief,
these devices are in most cases used solely for recording, instead of feeding intelligent analysis
processes capable of extracting information about the observed individuals. Thus, even though
video surveillance has already proved to be essential for solving multiple crimes, obtaining relevant
details about the subjects that took part in a crime depends on the manual inspection
of recordings. As such, the current goal of the research community is the development of
automated surveillance systems capable of monitoring and identifying subjects in surveillance
scenarios. Accordingly, the main goal of this thesis is to improve the performance of biometric
recognition algorithms in data acquired from surveillance scenarios. In particular, we aim at
designing a visual surveillance system capable of acquiring biometric data at a distance (e.g.,
face, iris or gait) without requiring human intervention in the process, as well as devising biometric
recognition methods robust to the degradation factors resulting from the unconstrained
acquisition process.
Regarding the first goal, the analysis of the data acquired by typical surveillance systems
shows that large acquisition distances significantly decrease the resolution of biometric samples,
and thus their discriminability is not sufficient for recognition purposes. In the literature,
diverse works point out Pan Tilt Zoom (PTZ) cameras as the most practical way for acquiring
high-resolution imagery at a distance, particularly when using a master-slave configuration. In
the master-slave configuration, the video acquired by a typical surveillance camera is analyzed
for obtaining regions of interest (e.g., car, person) and these regions are subsequently imaged
at high-resolution by the PTZ camera. Several methods have already shown that this configuration
can be used for acquiring biometric data at a distance. Nevertheless, these methods
failed at providing effective solutions to the typical challenges of this strategy, restraining its
use in surveillance scenarios. Accordingly, this thesis proposes two methods to support the development
of a biometric data acquisition system based on the cooperation of a PTZ camera
with a typical surveillance camera. The first proposal is a camera calibration method capable
of accurately mapping the coordinates of the master camera to the pan/tilt angles of the PTZ
camera. The second proposal is a camera scheduling method for determining - in real-time -
the sequence of acquisitions that maximizes the number of different targets obtained, while
minimizing the cumulative transition time. In order to achieve the first goal of this thesis,
both methods were combined with state-of-the-art approaches of the human monitoring field
to develop a fully automated surveillance capable of acquiring biometric data at a distance and
without human cooperation, designated as QUIS-CAMPI system.
The QUIS-CAMPI system is the basis for pursuing the second goal of this thesis. The analysis
of the performance of the state-of-the-art biometric recognition approaches shows that these
approaches attain almost ideal recognition rates in unconstrained data. However, this performance
is incongruous with the recognition rates observed in surveillance scenarios. Taking into
account the drawbacks of current biometric datasets, this thesis introduces a novel dataset comprising
biometric samples (face images and gait videos) acquired by the QUIS-CAMPI system at a
distance ranging from 5 to 40 meters and without human intervention in the acquisition process.
This set allows to objectively assess the performance of state-of-the-art biometric recognition
methods in data that truly encompass the covariates of surveillance scenarios. As such, this set
was exploited for promoting the first international challenge on biometric recognition in the wild. This thesis describes the evaluation protocols adopted, along with the results obtained
by the nine methods specially designed for this competition. In addition, the data acquired by
the QUIS-CAMPI system were crucial for accomplishing the second goal of this thesis, i.e., the
development of methods robust to the covariates of surveillance scenarios. The first proposal
regards a method for detecting corrupted features in biometric signatures inferred by a redundancy
analysis algorithm. The second proposal is a caricature-based face recognition approach
capable of enhancing the recognition performance by automatically generating a caricature
from a 2D photo. The experimental evaluation of these methods shows that both approaches
contribute to improve the recognition performance in unconstrained data.A crescente preocupação com a segurança dos indivĂduos tem justificado o crescimento
do nĂşmero de câmaras de vĂdeo-vigilância instaladas tanto em espaços privados como pĂşblicos.
Contudo, ao contrário do que normalmente se pensa, estes dispositivos são, na maior parte dos
casos, usados apenas para gravação, não estando ligados a nenhum tipo de software inteligente
capaz de inferir em tempo real informações sobre os indivĂduos observados. Assim, apesar de a
vĂdeo-vigilância ter provado ser essencial na resolução de diversos crimes, o seu uso está ainda
confinado Ă disponibilização de vĂdeos que tĂŞm que ser manualmente inspecionados para extrair
informações relevantes dos sujeitos envolvidos no crime. Como tal, atualmente, o principal
desafio da comunidade cientĂfica Ă© o desenvolvimento de sistemas automatizados capazes de
monitorizar e identificar indivĂduos em ambientes de vĂdeo-vigilância.
Esta tese tem como principal objetivo estender a aplicabilidade dos sistemas de reconhecimento
biomĂ©trico aos ambientes de vĂdeo-vigilância. De forma mais especifica, pretende-se
1) conceber um sistema de vĂdeo-vigilância que consiga adquirir dados biomĂ©tricos a longas distâncias
(e.g., imagens da cara, Ăris, ou vĂdeos do tipo de passo) sem requerer a cooperação dos
indivĂduos no processo; e 2) desenvolver mĂ©todos de reconhecimento biomĂ©trico robustos aos
fatores de degradação inerentes aos dados adquiridos por este tipo de sistemas.
No que diz respeito ao primeiro objetivo, a análise aos dados adquiridos pelos sistemas tĂpicos
de vĂdeo-vigilância mostra que, devido Ă distância de captura, os traços biomĂ©tricos amostrados
não são suficientemente discriminativos para garantir taxas de reconhecimento aceitáveis.
Na literatura, vários trabalhos advogam o uso de câmaras Pan Tilt Zoom (PTZ) para adquirir
imagens de alta resolução à distância, principalmente o uso destes dispositivos no modo masterslave.
Na configuração master-slave um módulo de análise inteligente seleciona zonas de interesse
(e.g. carros, pessoas) a partir do vĂdeo adquirido por uma câmara de vĂdeo-vigilância
e a câmara PTZ é orientada para adquirir em alta resolução as regiões de interesse. Diversos
métodos já mostraram que esta configuração pode ser usada para adquirir dados biométricos
à distância, ainda assim estes não foram capazes de solucionar alguns problemas relacionados
com esta estratĂ©gia, impedindo assim o seu uso em ambientes de vĂdeo-vigilância. Deste modo,
esta tese propõe dois métodos para permitir a aquisição de dados biométricos em ambientes de
vĂdeo-vigilância usando uma câmara PTZ assistida por uma câmara tĂpica de vĂdeo-vigilância. O
primeiro é um método de calibração capaz de mapear de forma exata as coordenadas da câmara
master para o ângulo da câmara PTZ (slave) sem o auxĂlio de outros dispositivos Ăłticos. O
segundo método determina a ordem pela qual um conjunto de sujeitos vai ser observado pela
câmara PTZ. O método proposto consegue determinar em tempo-real a sequência de observações
que maximiza o nĂşmero de diferentes sujeitos observados e simultaneamente minimiza o
tempo total de transição entre sujeitos. De modo a atingir o primeiro objetivo desta tese, os
dois métodos propostos foram combinados com os avanços alcançados na área da monitorização
de humanos para assim desenvolver o primeiro sistema de vĂdeo-vigilância completamente automatizado
e capaz de adquirir dados biométricos a longas distâncias sem requerer a cooperação
dos indivĂduos no processo, designado por sistema QUIS-CAMPI.
O sistema QUIS-CAMPI representa o ponto de partida para iniciar a investigação relacionada
com o segundo objetivo desta tese. A análise do desempenho dos métodos de reconhecimento
biométrico do estado-da-arte mostra que estes conseguem obter taxas de reconhecimento
quase perfeitas em dados adquiridos sem restrições (e.g., taxas de reconhecimento
maiores do que 99% no conjunto de dados LFW). Contudo, este desempenho nĂŁo Ă© corroborado pelos resultados observados em ambientes de vĂdeo-vigilância, o que sugere que os conjuntos
de dados atuais nĂŁo contĂŞm verdadeiramente os fatores de degradação tĂpicos dos ambientes de
vĂdeo-vigilância. Tendo em conta as vulnerabilidades dos conjuntos de dados biomĂ©tricos atuais,
esta tese introduz um novo conjunto de dados biomĂ©tricos (imagens da face e vĂdeos do tipo de
passo) adquiridos pelo sistema QUIS-CAMPI a uma distância máxima de 40m e sem a cooperação
dos sujeitos no processo de aquisição. Este conjunto permite avaliar de forma objetiva o desempenho
dos mĂ©todos do estado-da-arte no reconhecimento de indivĂduos em imagens/vĂdeos
capturados num ambiente real de vĂdeo-vigilância. Como tal, este conjunto foi utilizado para
promover a primeira competição de reconhecimento biométrico em ambientes não controlados.
Esta tese descreve os protocolos de avaliação usados, assim como os resultados obtidos por 9
métodos especialmente desenhados para esta competição. Para além disso, os dados adquiridos
pelo sistema QUIS-CAMPI foram essenciais para o desenvolvimento de dois métodos para
aumentar a robustez aos fatores de degradação observados em ambientes de vĂdeo-vigilância. O
primeiro Ă© um mĂ©todo para detetar caracterĂsticas corruptas em assinaturas biomĂ©tricas atravĂ©s
da análise da redundância entre subconjuntos de caracterĂsticas. O segundo Ă© um mĂ©todo de
reconhecimento facial baseado em caricaturas automaticamente geradas a partir de uma Ăşnica
foto do sujeito. As experiências realizadas mostram que ambos os métodos conseguem reduzir
as taxas de erro em dados adquiridos de forma nĂŁo controlada
Hierarchical and Spatial Structures for Interpreting Images of Man-made Scenes Using Graphical Models
The task of semantic scene interpretation is to label the regions of an image and their relations into meaningful classes. Such task is a key ingredient to many computer vision applications, including object recognition, 3D reconstruction and robotic perception. It is challenging partially due to the ambiguities inherent to the image data. The images of man-made scenes, e. g. the building facade images, exhibit strong contextual dependencies in the form of the spatial and hierarchical structures. Modelling these structures is central for such interpretation task. Graphical models provide a consistent framework for the statistical modelling. Bayesian networks and random fields are two popular types of the graphical models, which are frequently used for capturing such contextual information. The motivation for our work comes from the belief that we can find a generic formulation for scene interpretation that having both the benefits from random fields and Bayesian networks. It should have clear semantic interpretability. Therefore our key contribution is the development of a generic statistical graphical model for scene interpretation, which seamlessly integrates different types of the image features, and the spatial structural information and the hierarchical structural information defined over the multi-scale image segmentation. It unifies the ideas of existing approaches, e. g. conditional random field (CRF) and Bayesian network (BN), which has a clear statistical interpretation as the maximum a posteriori (MAP) estimate of a multi-class labelling problem. Given the graphical model structure, we derive the probability distribution of the model based on the factorization property implied in the model structure. The statistical model leads to an energy function that can be optimized approximately by either loopy belief propagation or graph cut based move making algorithm. The particular type of the features, the spatial structure, and the hierarchical structure however is not prescribed. In the experiments, we concentrate on terrestrial man-made scenes as a specifically difficult problem. We demonstrate the application of the proposed graphical model on the task of multi-class classification of building facade image regions. The framework for scene interpretation allows for significantly better classification results than the standard classical local classification approach on man-made scenes by incorporating the spatial and hierarchical structures. We investigate the performance of the algorithms on a public dataset to show the relative importance of the information from the spatial structure and the hierarchical structure. As a baseline for the region classification, we use an efficient randomized decision forest classifier. Two specific models are derived from the proposed graphical model, namely the hierarchical CRF and the hierarchical mixed graphical model. We show that these two models produce better classification results than both the baseline region classifier and the flat CRF.Hierarchische und räumliche Strukturen zur Interpretation von Bildern anthropogener Szenen unter Nutzung graphischer Modelle Ziel der semantischen Bildinterpretation ist es, Bildregionen und ihre gegenseitigen Beziehungen zu kennzeichnen und in sinnvolle Klassen einzuteilen. Dies ist eine der Hauptaufgabe in vielen Bereichen des maschinellen Sehens, wie zum Beispiel der Objekterkennung, 3D Rekonstruktion oder der Wahrnehmung von Robotern. Insbesondere Bilder anthropogener Szenen, wie z.B. Fassadenaufnahmen, sind durch starke räumliche und hierarchische Strukturen gekennzeichnet. Diese Strukturen zu modellieren ist zentrale Teil der Interpretation, für deren statistische Modellierung graphische Modelle ein geeignetes konsistentes Werkzeug darstellen. Bayes Netze und Zufallsfelder sind zwei bekannte und häufig genutzte Beispiele für graphische Modelle zur Erfassung kontextabhängiger Informationen. Die Motivation dieser Arbeit liegt in der überzeugung, dass wir eine generische Formulierung der Bildinterpretation mit klarer semantischer Bedeutung finden können, die die Vorteile von Bayes Netzen und Zufallsfeldern verbindet. Der Hauptbeitrag der vorliegenden Arbeit liegt daher in der Entwicklung eines generischen statistischen graphischen Modells zur Bildinterpretation, welches unterschiedlichste Typen von Bildmerkmalen und die räumlichen sowie hierarchischen Strukturinformationen über eine multiskalen Bildsegmentierung integriert. Das Modell vereinheitlicht die existierender Arbeiten zugrunde liegenden Ideen, wie bedingter Zufallsfelder (conditional random field (CRF)) und Bayesnetze (Bayesian network (BN)). Dieses Modell hat eine klare statistische Interpretation als Maximum a posteriori (MAP) Schätzer eines mehrklassen Zuordnungsproblems. Gegeben die Struktur des graphischen Modells und den dadurch definierten Faktorisierungseigenschaften leiten wir die Wahrscheinlichkeitsverteilung des Modells ab. Dies führt zu einer Energiefunktion, die näherungsweise optimiert werden kann. Der jeweilige Typ der Bildmerkmale, die räumliche sowie hierarchische Struktur ist von dieser Formulierung unabhängig. Wir zeigen die Anwendung des vorgeschlagenen graphischen Modells anhand der mehrklassen Zuordnung von Bildregionen in Fassadenaufnahmen. Wir demonstrieren, dass das vorgeschlagene Verfahren zur Bildinterpretation, durch die Berücksichtigung räumlicher sowie hierarchischer Strukturen, signifikant bessere Klassifikationsergebnisse zeigt, als klassische lokale Klassifikationsverfahren. Die Leistungsfähigkeit des vorgeschlagenen Verfahrens wird anhand eines öffentlich verfügbarer Datensatzes evaluiert. Zur Klassifikation der Bildregionen nutzen wir ein Verfahren basierend auf einem effizienten Random Forest Klassifikator. Aus dem vorgeschlagenen allgemeinen graphischen Modell werden konkret zwei spezielle Modelle abgeleitet, ein hierarchisches bedingtes Zufallsfeld (hierarchical CRF) sowie ein hierarchisches gemischtes graphisches Modell. Wir zeigen, dass beide Modelle bessere Klassifikationsergebnisse erzeugen als die zugrunde liegenden lokalen Klassifikatoren oder die einfachen bedingten Zufallsfelder
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