Computational Methods for Measurement of Visual Attention from Videos towards Large-Scale Behavioral Analysis

Visual attention is one of the most important aspects of human social behavior, visual navigation, and interaction with the world, revealing information about their social, cognitive, and affective states. Although monitor-based and wearable eye trackers are widely available, they are not sufficient to support the large-scale collection of naturalistic gaze data in face-to-face social interactions or during interactions with 3D environments. Wearable eye trackers are burdensome to participants and bring issues of calibration, compliance, cost, and battery life. The ability to automatically measure attention from ordinary videos would deliver scalable, dense, and objective measurements to use in practice. This thesis investigates several computational methods to measure visual attention from videos using computer vision and its use for quantifying visual social cues such as eye contact and joint attention. Specifically, three methods are investigated. First, I present methods for detection of looks to camera in first-person view and its use for eye contact detection. Experimental results show that the presented method can achieve the first human expert-level detection of eye contact. Second, I develop a method for tracking heads in a 3d space for measuring attentional shifts. Lastly, I propose spatiotemporal deep neural networks for detecting time-varying attention targets in video and present its application for the detection of shared attention and joint attention. The method achieves state-of-the-art results on different benchmark datasets on attention measurement as well as the first empirical result on clinically-relevant gaze shift classification. Presented approaches have the benefit of linking gaze estimation to the broader tasks of action recognition and dynamic visual scene understanding, and bears potential as a useful tool for understanding attention in various contexts such as human social interactions, skill assessments, and human-robot interactions.Ph.D

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

The relationship between the home environment and children’s physical activity and sedentary behaviour at home

Increasing children’s physical activity (PA) and reducing their sedentary behaviour are considered important preventative measures for obesity and several other health risk factors in children. Given children spend significant time at home, an improved understanding of these behaviours in the home environment would provide invaluable insight for interventions. Therefore, the overarching aim of this thesis was to provide new insight into how the home environment is related to children’s home-based PA and sedentary behaviour. Study 1 investigated the relationship between sufficient moderate-to-vigorous physical activity (MVPA) (≥60 min·day–1) and excessive screen-time (≥2 h·day–1) with lifestyle factors in children, and found they were associated with healthy and unhealthy factors, respectively. This study highlighted the importance of meeting PA and screen-time recommendations in relation to important health-related lifestyle factors, which is of concern, as few children were shown to meet such recommendations. Identifying the correlates of children’s behaviours is an important stage in intervention development, therefore studies 2-5 focussed on improving understanding of children’s PA and sedentary behaviour at home. Study 2 demonstrated the validity and reliability of HomeSPACE-II, a novel instrument for measuring physical factors that influence children’s home-based PA and sedentary behaviour. Using HomeSPACE-II, study 3 showed that the physical home environment is related to children’s home-based PA and sedentary behaviour. Given the established influence of social and individual factors on children’s behaviour and their confounding effects in study 3, study 4 investigated the influence of social and individual factors on: (i) children’s home-based PA and sedentary behaviour, and; (ii) the home physical environment. Study 4 revealed that parental and child activity preferences and priorities, as well as parental rules were associated with children’s home-based PA and sedentary behaviour and the physical home environment. Study 5 found clusters of social and physical factors at home, which were associated with children’s home-based PA and sedentary behaviour as well as background characteristics in the expected directions

Multi-View Digital Representation Of Social Behaviours In Children And Action Recognition Methods

Autism spectrum disorders (ASD) affect at least 1% of children globally. It is partially defined by social behaviour delays in eye contact and joint attention during social interaction and with evidence of reduced heart rate variability (HRV) under static and social stress environments. Currently, no validated artificial intelligence or signal processing algorithms are available to objectively quantify behavioural and physiological markers in unrestricted interactive play environments to assist in the diagnosis of ASD. This thesis proposes that social behavioural and physiological markers of children with ASD can be objectively quantified through a synergistic digital approach from multi-modal and multi-view data sources. First, a novel deep learning (DL) framework for social behaviour recognition using a fusion of multi-view and multi-modal predictions is proposed. It utilises true-colour images and moving trajectory (optical flow) images extracted from fixed camera video recordings to detect eye contact between children and caregivers in free play while elucidating unique digital features of eye contact behaviour in multiple individual social interaction settings. Moreover, for the first time, a support vector machine model with feature selection is implemented along with statistical analysis, to identify effective facial features and facial orientations for use in identifying ASD during joint attention episodes in free play. Furthermore, a customised NeuroKit2 toolbox was validated using the opensource QT database and a clinical baseline social interaction task. This toolbox facilitates the automated extraction of HRV metrics and allows between-group comparisons in physiological markers. The work highlights the importance of developing explainable algorithms that objectively quantifying multi-modal digital markers. It offers the potential for the use of digitalised phenotypes to aid in the assessment of ASD and intervention in naturalistic social interaction

Measuring Behavior 2018 Conference Proceedings

These proceedings contain the papers presented at Measuring Behavior 2018, the 11th International Conference on Methods and Techniques in Behavioral Research. The conference was organised by Manchester Metropolitan University, in collaboration with Noldus Information Technology. The conference was held during June 5th – 8th, 2018 in Manchester, UK. Building on the format that has emerged from previous meetings, we hosted a fascinating program about a wide variety of methodological aspects of the behavioral sciences. We had scientific presentations scheduled into seven general oral sessions and fifteen symposia, which covered a topical spread from rodent to human behavior. We had fourteen demonstrations, in which academics and companies demonstrated their latest prototypes. The scientific program also contained three workshops, one tutorial and a number of scientific discussion sessions. We also had scientific tours of our facilities at Manchester Metropolitan Univeristy, and the nearby British Cycling Velodrome. We hope this proceedings caters for many of your interests and we look forward to seeing and hearing more of your contributions