101 research outputs found
Advances in Monocular Exemplar-based Human Body Pose Analysis: Modeling, Detection and Tracking
Esta tesis contribuye en el análisis de la postura del cuerpo humano a partir de secuencias de imágenes adquiridas con una sola cámara. Esta temática presenta un amplio rango de potenciales aplicaciones en video-vigilancia, video-juegos o aplicaciones biomédicas. Las técnicas basadas en patrones han tenido éxito, sin embargo, su precisión depende de la similitud del punto de vista de la cámara y de las propiedades de la escena entre las imágenes de entrenamiento y las de prueba. Teniendo en cuenta un conjunto de datos de entrenamiento capturado mediante un número reducido de cámaras fijas, paralelas al suelo, se han identificado y analizado tres escenarios posibles con creciente nivel de dificultad: 1) una cámara estática paralela al suelo, 2) una cámara de vigilancia fija con un ángulo de visión considerablemente diferente, y 3) una secuencia de video capturada con una cámara en movimiento o simplemente una sola imagen estática
Improved robustness and efficiency for automatic visual site monitoring
Thesis (Ph. D.)--Massachusetts Institute of Technology, Dept. of Electrical Engineering and Computer Science, 2009.This electronic version was submitted by the student author. The certified thesis is available in the Institute Archives and Special Collections.Cataloged from student-submitted PDF version of thesis.Includes bibliographical references (p. 219-228).Knowing who people are, where they are, what they are doing, and how they interact with other people and things is valuable from commercial, security, and space utilization perspectives. Video sensors backed by computer vision algorithms are a natural way to gather this data. Unfortunately, key technical issues persist in extracting features and models that are simultaneously efficient to compute and robust to issues such as adverse lighting conditions, distracting background motions, appearance changes over time, and occlusions. In this thesis, we present a set of techniques and model enhancements to better handle these problems, focusing on contributions in four areas. First, we improve background subtraction so it can better handle temporally irregular dynamic textures. This allows us to achieve a 5.5% drop in false positive rate on the Wallflower waving trees video. Secondly, we adapt the Dalal and Triggs Histogram of Oriented Gradients pedestrian detector to work on large-scale scenes with dense crowds and harsh lighting conditions: challenges which prevent us from easily using a background subtraction solution. These scenes contain hundreds of simultaneously visible people. To make using the algorithm computationally feasible, we have produced a novel implementation that runs on commodity graphics hardware and is up to 76 faster than our CPU-only implementation. We demonstrate the utility of this detector by modeling scene-level activities with a Hierarchical Dirichlet Process.(cont.) Third, we show how one can improve the quality of pedestrian silhouettes for recognizing individual people. We combine general appearance information from a large population of pedestrians with semi-periodic shape information from individual silhouette sequences. Finally, we show how one can combine a variety of detection and tracking techniques to robustly handle a variety of event detection scenarios such as theft and left-luggage detection. We present the only complete set of results on a standardized collection of very challenging videos.by Gerald Edwin Dalley.Ph.D
Hierarchische Modelle für das visuelle Erkennen und Lernen von Objekten, Szenen und Aktivitäten
In many computer vision applications, objects have to be learned and recognized in images or image sequences. Most of these objects have a hierarchical structure.For example, 3d objects can be decomposed into object parts, and object parts, in turn, into geometric primitives. Furthermore, scenes are composed of objects. And also activities or behaviors can be divided hierarchically into actions, these into individual movements, etc. Hierarchical models are therefore ideally suited for the representation of a wide range of objects used in applications such as object recognition, human pose estimation, or activity recognition.
In this work new probabilistic hierarchical models are presented that allow an efficient representation of multiple objects of different categories, scales, rotations, and views. The idea is to exploit similarities between objects, object parts or actions and movements in order to share calculations and avoid redundant information. We will introduce online and offline learning methods, which enable to create efficient hierarchies based on small or large training datasets, in which poses or articulated structures are given by instances. Furthermore, we present inference approaches for fast and robust detection. These new approaches combine the idea of compositional and similarity hierarchies and overcome limitations of previous methods. They will be used in an unified hierarchical framework spatially for object recognition as well as spatiotemporally for activity recognition.
The unified generic hierarchical framework allows us to apply the proposed models in different projects. Besides classical object recognition it is used for detection of human poses in a project for gait analysis. The activity detection is used in a project for the design of environments for ageing, to identify activities and behavior patterns in smart homes. In a project for parking spot detection using an intelligent vehicle, the proposed approaches are used to hierarchically model the environment of the vehicle for an efficient and robust interpretation of the scene in real-time.In zahlreichen Computer Vision Anwendungen müssen Objekte in einzelnen Bildern oder Bildsequenzen erlernt und erkannt werden. Viele dieser Objekte sind hierarchisch aufgebaut.So lassen sich 3d Objekte in Objektteile zerlegen und Objektteile wiederum in geometrische Grundkörper. Und auch Aktivitäten oder Verhaltensmuster lassen sich hierarchisch in einzelne Aktionen aufteilen, diese wiederum in einzelne Bewegungen usw. Für die Repräsentation sind hierarchische Modelle dementsprechend gut geeignet.
In dieser Arbeit werden neue probabilistische hierarchische Modelle vorgestellt, die es ermöglichen auch mehrere Objekte verschiedener Kategorien, Skalierungen, Rotationen und aus verschiedenen Blickrichtungen effizient zu repräsentieren. Eine Idee ist hierbei, Ähnlichkeiten unter Objekten, Objektteilen oder auch Aktionen und Bewegungen zu nutzen, um redundante Informationen und Mehrfachberechnungen zu vermeiden. In der Arbeit werden online und offline Lernverfahren vorgestellt, die es ermöglichen, effiziente Hierarchien auf Basis von kleinen oder großen Trainingsdatensätzen zu erstellen, in denen Posen und bewegliche Strukturen durch Beispiele gegeben sind. Des Weiteren werden Inferenzansätze zur schnellen und robusten Detektion vorgestellt. Diese werden innerhalb eines einheitlichen hierarchischen Frameworks sowohl räumlich zur Objekterkennung als auch raumzeitlich zur Aktivitätenerkennung verwendet.
Das einheitliche Framework ermöglicht die Anwendung des vorgestellten Modells innerhalb verschiedener Projekte. Neben der klassischen Objekterkennung wird es zur Erkennung von menschlichen Posen in einem Projekt zur Ganganalyse verwendet. Die Aktivitätenerkennung wird in einem Projekt zur Gestaltung altersgerechter Lebenswelten genutzt, um in intelligenten Wohnräumen Aktivitäten und Verhaltensmuster von Bewohnern zu erkennen. Im Rahmen eines Projektes zur Parklückenvermessung mithilfe eines intelligenten Fahrzeuges werden die vorgestellten Ansätze verwendet, um das Umfeld des Fahrzeuges hierarchisch zu modellieren und dadurch das Szenenverstehen zu ermöglichen
Recommended from our members
A Review of Techniques on Gait-Based Person Re-Identification
Copyright (c) 2023 Babak Rahi, Maozhen Li and Man Qi. Person re-identification at a distance across multiple non-overlapping cameras has been an active research area for years. In the past ten years, short-term Person re-identification techniques have made great strides in accuracy using only appearance features in limited environments. However, massive intra-class variations and inter-class confusion limit their ability to be used in practical applications. Moreover, appearance consistency can only be assumed in a short time span from one camera to the other. Since the holistic appearance will change drastically over days and weeks, the technique, as mentioned above, will be ineffective. Practical applications usually require a long-term solution in which the subject's appearance and clothing might have changed after the elapse of a significant period. Facing these problems, soft biometric features such as Gait has stirred much interest in the past years. Nevertheless, even Gait can vary with illness, ageing and emotional states, walking surfaces, shoe types, clothes types, carried objects (by the subject) and even environment clutters. Therefore, Gait is considered as a temporal cue that could provide biometric motion information. On the other hand, the shape of the human body could be viewed as a spatial signal which can produce valuable information. So extracting discriminative features from both spatial and temporal domains would benefit this research. This article examines the main approaches used in gait analysis for re-identification over the past decade. We identify several relevant dimensions of the problem and provide a taxonomic analysis of current research. We conclude by reviewing the performance levels achievable with current technology and providing a perspective on the most challenging and promising research directions.This research received no external funding
Soft Biometric Analysis: MultiPerson and RealTime Pedestrian Attribute Recognition in Crowded Urban Environments
Traditionally, recognition systems were only based on human hard biometrics. However,
the ubiquitous CCTV cameras have raised the desire to analyze human biometrics from
far distances, without people attendance in the acquisition process. Highresolution
face closeshots
are rarely available at far distances such that facebased
systems cannot
provide reliable results in surveillance applications. Human soft biometrics such as body
and clothing attributes are believed to be more effective in analyzing human data collected
by security cameras.
This thesis contributes to the human soft biometric analysis in uncontrolled environments
and mainly focuses on two tasks: Pedestrian Attribute Recognition (PAR) and person reidentification
(reid).
We first review the literature of both tasks and highlight the history
of advancements, recent developments, and the existing benchmarks. PAR and person reid
difficulties are due to significant distances between intraclass
samples, which originate
from variations in several factors such as body pose, illumination, background, occlusion,
and data resolution. Recent stateoftheart
approaches present endtoend
models that
can extract discriminative and comprehensive feature representations from people. The
correlation between different regions of the body and dealing with limited learning data
is also the objective of many recent works. Moreover, class imbalance and correlation
between human attributes are specific challenges associated with the PAR problem.
We collect a large surveillance dataset to train a novel gender recognition model suitable
for uncontrolled environments. We propose a deep residual network that extracts several
posewise
patches from samples and obtains a comprehensive feature representation. In
the next step, we develop a model for multiple attribute recognition at once. Considering
the correlation between human semantic attributes and class imbalance, we respectively
use a multitask
model and a weighted loss function. We also propose a multiplication
layer on top of the backbone features extraction layers to exclude the background features
from the final representation of samples and draw the attention of the model to the
foreground area.
We address the problem of person reid
by implicitly defining the receptive fields of
deep learning classification frameworks. The receptive fields of deep learning models
determine the most significant regions of the input data for providing correct decisions.
Therefore, we synthesize a set of learning data in which the destructive regions (e.g.,
background) in each pair of instances are interchanged. A segmentation module
determines destructive and useful regions in each sample, and the label of synthesized
instances are inherited from the sample that shared the useful regions in the synthesized
image. The synthesized learning data are then used in the learning phase and help
the model rapidly learn that the identity and background regions are not correlated.
Meanwhile, the proposed solution could be seen as a data augmentation approach that
fully preserves the label information and is compatible with other data augmentation
techniques.
When reid
methods are learned in scenarios where the target person appears with identical garments in the gallery, the visual appearance of clothes is given the most
importance in the final feature representation. Clothbased
representations are not
reliable in the longterm
reid
settings as people may change their clothes. Therefore,
developing solutions that ignore clothing cues and focus on identityrelevant
features are
in demand. We transform the original data such that the identityrelevant
information of
people (e.g., face and body shape) are removed, while the identityunrelated
cues (i.e.,
color and texture of clothes) remain unchanged. A learned model on the synthesized
dataset predicts the identityunrelated
cues (shortterm
features). Therefore, we train a
second model coupled with the first model and learns the embeddings of the original data
such that the similarity between the embeddings of the original and synthesized data is
minimized. This way, the second model predicts based on the identityrelated
(longterm)
representation of people.
To evaluate the performance of the proposed models, we use PAR and person reid
datasets, namely BIODI, PETA, RAP, Market1501,
MSMTV2,
PRCC, LTCC, and MIT
and compared our experimental results with stateoftheart
methods in the field.
In conclusion, the data collected from surveillance cameras have low resolution, such
that the extraction of hard biometric features is not possible, and facebased
approaches
produce poor results. In contrast, soft biometrics are robust to variations in data quality.
So, we propose approaches both for PAR and person reid
to learn discriminative features
from each instance and evaluate our proposed solutions on several publicly available
benchmarks.This thesis was prepared at the University of Beria Interior, IT Instituto de Telecomunicações, Soft Computing and Image Analysis Laboratory (SOCIA Lab), Covilhã Delegation, and was submitted to the University of Beira Interior for defense in a public examination session
Person re-Identification over distributed spaces and time
PhDReplicating the human visual system and cognitive abilities that the brain uses to process the
information it receives is an area of substantial scientific interest. With the prevalence of video
surveillance cameras a portion of this scientific drive has been into providing useful automated
counterparts to human operators. A prominent task in visual surveillance is that of matching
people between disjoint camera views, or re-identification. This allows operators to locate people
of interest, to track people across cameras and can be used as a precursory step to multi-camera
activity analysis. However, due to the contrasting conditions between camera views and their
effects on the appearance of people re-identification is a non-trivial task. This thesis proposes
solutions for reducing the visual ambiguity in observations of people between camera views
This thesis first looks at a method for mitigating the effects on the appearance of people under
differing lighting conditions between camera views. This thesis builds on work modelling
inter-camera illumination based on known pairs of images. A Cumulative Brightness Transfer
Function (CBTF) is proposed to estimate the mapping of colour brightness values based on limited
training samples. Unlike previous methods that use a mean-based representation for a set of
training samples, the cumulative nature of the CBTF retains colour information from underrepresented
samples in the training set. Additionally, the bi-directionality of the mapping function
is explored to try and maximise re-identification accuracy by ensuring samples are accurately
mapped between cameras.
Secondly, an extension is proposed to the CBTF framework that addresses the issue of changing
lighting conditions within a single camera. As the CBTF requires manually labelled training
samples it is limited to static lighting conditions and is less effective if the lighting changes. This
Adaptive CBTF (A-CBTF) differs from previous approaches that either do not consider lighting
change over time, or rely on camera transition time information to update. By utilising contextual
information drawn from the background in each camera view, an estimation of the lighting
change within a single camera can be made. This background lighting model allows the mapping
of colour information back to the original training conditions and thus remove the need for
3
retraining.
Thirdly, a novel reformulation of re-identification as a ranking problem is proposed. Previous
methods use a score based on a direct distance measure of set features to form a correct/incorrect
match result. Rather than offering an operator a single outcome, the ranking paradigm is to give
the operator a ranked list of possible matches and allow them to make the final decision. By utilising
a Support Vector Machine (SVM) ranking method, a weighting on the appearance features
can be learned that capitalises on the fact that not all image features are equally important to
re-identification. Additionally, an Ensemble-RankSVM is proposed to address scalability issues
by separating the training samples into smaller subsets and boosting the trained models.
Finally, the thesis looks at a practical application of the ranking paradigm in a real world application.
The system encompasses both the re-identification stage and the precursory extraction
and tracking stages to form an aid for CCTV operators. Segmentation and detection are combined
to extract relevant information from the video, while several combinations of matching
techniques are combined with temporal priors to form a more comprehensive overall matching
criteria.
The effectiveness of the proposed approaches is tested on datasets obtained from a variety
of challenging environments including offices, apartment buildings, airports and outdoor public
spaces
Pedestrian soft biometrics recognition using deep learning on thermal images in smart cities
With technological advancement and the rise of the Internet of Things, our society is becoming more interconnected than ever before. Our computers and devices are getting smaller, and their computing power and memory has been increasing. These advances coupled with the leaps in artificial intelligence caused by the deep learning revolution in recent yearshave led to an increasingly rising interest in the field of pervasive intelligence. Intelligence in the environment has been used in smart homes in order to bring assistance to semi-autonomous people by performing activity recognition based on sensor data. As technology keeps improving, we may start to investigate the extension of assistive technologies beyond the boundaries of smart homes and into our smart cities. In order to bring assistance to semi-autonomous people, the first step is to be able to recognize profiles of vulnerable people. In order to leverage technology and artificial intelligence to make our cities smarter, safer and more accessible, this thesis investigates the use of environmental sensors such as thermal cameras to perform pedestrian soft biometrics recognition (age, gender and mobility) in the city. In this thesis, the process of building prototypes from scratch in order to collect thermal gait data in the city is explored, and the use and optimization of deep learning algorithms to perform soft biometrics recognition, as well as the feasibility of implementing these algorithms on limited resource boards are explored. The use of unprocessed thermal images allows a higher degree of privacy for the citizens, and it is novel in the field of human profile recognition. This thesis aims to set the foundation of future work, both in the field of thermal images-based soft biometrics recognition and pervasive intelligence in our cities in order to make them smarter, and move towards an interconnected society.
Les progrès technologiques et le développement de l’Internet des Objets nous mènent vers une société de plus en plus interconnectée. Nos ordinateurs et nos appareils deviennent de plus en plus petits et leur puissance de calcul et leur mémoire ne cesse de s’améliorer. Ces avancées combinées aux récents progrès dans le domaine de l’intelligence artificielle avec la révolution de l’apprentissage profond ont mené à un intérêt grandissant dans le domaine de l’intelligence ambiante. L’intelligence ambiante a été utilisée dans le domaine des maisons intelligentes sous forme de reconnaissance d’activités, permettant d’assister les personnes semi-autonomes en utilisant des données collectées par des capteurs. Alors que le progrès technologique continue, nous arrivons à un point où l’hypothèse d’étendre ces stratégies d’assistance des maisons aux villes intelligentes devient de plus en plus réaliste. Afin d’étendre cette assistance aux villes, la première étape est d’identifier les personnes vulnérables, qui sont celles qui pourraient bénéficier de cette assistance. Dans le but d’utiliser la technologie pour rendre nos villes plus intelligentes, plus sûres et plus accessibles, cette thèse explore l’utilisation de capteurs environnementaux tels que des caméras thermiques pour effectuer de la reconnaissance de profils dans la ville (âge, genre et mobilité). Dans cette thèse, le processus de construction de prototypes pour récolter des données thermales dans la ville est présenté, et l’utilisation ainsi que l’optimisation d’algorithmes d’apprentissage profond pour la reconnaissance de profils est explorée. L’implémentation des algorithmes sur un système embarqué est également abordée. L’utilisation d’images thermiques garantit un plus grand degré d’anonymat pour les citoyens que l’utilisation de caméras RGB, et cette thèse représente les premiers travaux de reconnaissance de profils multiples en utilisant uniquement des images thermiques sans pré-traitement. Cette thèse a pour objectif de poser les bases pour des travaux futurs dans le domaine de la reconnaissance de profils en utilisant des images thermiques, ainsi que dans le domaine de l’intelligence ambiante dans nos villes, afin de les rendre plus intelligentes et de se diriger vers une société interconnectée
- …