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

Take an Emotion Walk: Perceiving Emotions from Gaits Using Hierarchical Attention Pooling and Affective Mapping

We present an autoencoder-based semi-supervised approach to classify perceived human emotions from walking styles obtained from videos or motion-captured data and represented as sequences of 3D poses. Given the motion on each joint in the pose at each time step extracted from 3D pose sequences, we hierarchically pool these joint motions in a bottom-up manner in the encoder, following the kinematic chains in the human body. We also constrain the latent embeddings of the encoder to contain the space of psychologically-motivated affective features underlying the gaits. We train the decoder to reconstruct the motions per joint per time step in a top-down manner from the latent embeddings. For the annotated data, we also train a classifier to map the latent embeddings to emotion labels. Our semi-supervised approach achieves a mean average precision of 0.84 on the Emotion-Gait benchmark dataset, which contains both labeled and unlabeled gaits collected from multiple sources. We outperform current state-of-art algorithms for both emotion recognition and action recognition from 3D gaits by 7%--23% on the absolute. More importantly, we improve the average precision by 10%--50% on the absolute on classes that each makes up less than 25% of the labeled part of the Emotion-Gait benchmark dataset.Comment: In proceedings of the 16th European Conference on Computer Vision, 2020. Total pages 18. Total figures 5. Total tables

Multi-Robot Systems: Challenges, Trends and Applications

This book is a printed edition of the Special Issue entitled “Multi-Robot Systems: Challenges, Trends, and Applications” that was published in Applied Sciences. This Special Issue collected seventeen high-quality papers that discuss the main challenges of multi-robot systems, present the trends to address these issues, and report various relevant applications. Some of the topics addressed by these papers are robot swarms, mission planning, robot teaming, machine learning, immersive technologies, search and rescue, and social robotics

Sensing, interpreting, and anticipating human social behaviour in the real world

Low-level nonverbal social signals like glances, utterances, facial expressions and body language are central to human communicative situations and have been shown to be connected to important high-level constructs, such as emotions, turn-taking, rapport, or leadership. A prerequisite for the creation of social machines that are able to support humans in e.g. education, psychotherapy, or human resources is the ability to automatically sense, interpret, and anticipate human nonverbal behaviour. While promising results have been shown in controlled settings, automatically analysing unconstrained situations, e.g. in daily-life settings, remains challenging. Furthermore, anticipation of nonverbal behaviour in social situations is still largely unexplored. The goal of this thesis is to move closer to the vision of social machines in the real world. It makes fundamental contributions along the three dimensions of sensing, interpreting and anticipating nonverbal behaviour in social interactions. First, robust recognition of low-level nonverbal behaviour lays the groundwork for all further analysis steps. Advancing human visual behaviour sensing is especially relevant as the current state of the art is still not satisfactory in many daily-life situations. While many social interactions take place in groups, current methods for unsupervised eye contact detection can only handle dyadic interactions. We propose a novel unsupervised method for multi-person eye contact detection by exploiting the connection between gaze and speaking turns. Furthermore, we make use of mobile device engagement to address the problem of calibration drift that occurs in daily-life usage of mobile eye trackers. Second, we improve the interpretation of social signals in terms of higher level social behaviours. In particular, we propose the first dataset and method for emotion recognition from bodily expressions of freely moving, unaugmented dyads. Furthermore, we are the first to study low rapport detection in group interactions, as well as investigating a cross-dataset evaluation setting for the emergent leadership detection task. Third, human visual behaviour is special because it functions as a social signal and also determines what a person is seeing at a given moment in time. Being able to anticipate human gaze opens up the possibility for machines to more seamlessly share attention with humans, or to intervene in a timely manner if humans are about to overlook important aspects of the environment. We are the first to propose methods for the anticipation of eye contact in dyadic conversations, as well as in the context of mobile device interactions during daily life, thereby paving the way for interfaces that are able to proactively intervene and support interacting humans.Blick, Gesichtsausdrücke, Körpersprache, oder Prosodie spielen als nonverbale Signale eine zentrale Rolle in menschlicher Kommunikation. Sie wurden durch vielzählige Studien mit wichtigen Konzepten wie Emotionen, Sprecherwechsel, Führung, oder der Qualität des Verhältnisses zwischen zwei Personen in Verbindung gebracht. Damit Menschen effektiv während ihres täglichen sozialen Lebens von Maschinen unterstützt werden können, sind automatische Methoden zur Erkennung, Interpretation, und Antizipation von nonverbalem Verhalten notwendig. Obwohl die bisherige Forschung in kontrollierten Studien zu ermutigenden Ergebnissen gekommen ist, bleibt die automatische Analyse nonverbalen Verhaltens in weniger kontrollierten Situationen eine Herausforderung. Darüber hinaus existieren kaum Untersuchungen zur Antizipation von nonverbalem Verhalten in sozialen Situationen. Das Ziel dieser Arbeit ist, die Vision vom automatischen Verstehen sozialer Situationen ein Stück weit mehr Realität werden zu lassen. Diese Arbeit liefert wichtige Beiträge zur autmatischen Erkennung menschlichen Blickverhaltens in alltäglichen Situationen. Obwohl viele soziale Interaktionen in Gruppen stattfinden, existieren unüberwachte Methoden zur Augenkontakterkennung bisher lediglich für dyadische Interaktionen. Wir stellen einen neuen Ansatz zur Augenkontakterkennung in Gruppen vor, welcher ohne manuelle Annotationen auskommt, indem er sich den statistischen Zusammenhang zwischen Blick- und Sprechverhalten zu Nutze macht. Tägliche Aktivitäten sind eine Herausforderung für Geräte zur mobile Augenbewegungsmessung, da Verschiebungen dieser Geräte zur Verschlechterung ihrer Kalibrierung führen können. In dieser Arbeit verwenden wir Nutzerverhalten an mobilen Endgeräten, um den Effekt solcher Verschiebungen zu korrigieren. Neben der Erkennung verbessert diese Arbeit auch die Interpretation sozialer Signale. Wir veröffentlichen den ersten Datensatz sowie die erste Methode zur Emotionserkennung in dyadischen Interaktionen ohne den Einsatz spezialisierter Ausrüstung. Außerdem stellen wir die erste Studie zur automatischen Erkennung mangelnder Verbundenheit in Gruppeninteraktionen vor, und führen die erste datensatzübergreifende Evaluierung zur Detektion von sich entwickelndem Führungsverhalten durch. Zum Abschluss der Arbeit präsentieren wir die ersten Ansätze zur Antizipation von Blickverhalten in sozialen Interaktionen. Blickverhalten hat die besondere Eigenschaft, dass es sowohl als soziales Signal als auch der Ausrichtung der visuellen Wahrnehmung dient. Somit eröffnet die Fähigkeit zur Antizipation von Blickverhalten Maschinen die Möglichkeit, sich sowohl nahtloser in soziale Interaktionen einzufügen, als auch Menschen zu warnen, wenn diese Gefahr laufen wichtige Aspekte der Umgebung zu übersehen. Wir präsentieren Methoden zur Antizipation von Blickverhalten im Kontext der Interaktion mit mobilen Endgeräten während täglicher Aktivitäten, als auch während dyadischer Interaktionen mittels Videotelefonie