3D Robotic Sensing of People: Human Perception, Representation and Activity Recognition

The robots are coming. Their presence will eventually bridge the digital-physical divide and dramatically impact human life by taking over tasks where our current society has shortcomings (e.g., search and rescue, elderly care, and child education). Human-centered robotics (HCR) is a vision to address how robots can coexist with humans and help people live safer, simpler and more independent lives. As humans, we have a remarkable ability to perceive the world around us, perceive people, and interpret their behaviors. Endowing robots with these critical capabilities in highly dynamic human social environments is a significant but very challenging problem in practical human-centered robotics applications. This research focuses on robotic sensing of people, that is, how robots can perceive and represent humans and understand their behaviors, primarily through 3D robotic vision. In this dissertation, I begin with a broad perspective on human-centered robotics by discussing its real-world applications and significant challenges. Then, I will introduce a real-time perception system, based on the concept of Depth of Interest, to detect and track multiple individuals using a color-depth camera that is installed on moving robotic platforms. In addition, I will discuss human representation approaches, based on local spatio-temporal features, including new “CoDe4D” features that incorporate both color and depth information, a new “SOD” descriptor to efficiently quantize 3D visual features, and the novel AdHuC features, which are capable of representing the activities of multiple individuals. Several new algorithms to recognize human activities are also discussed, including the RG-PLSA model, which allows us to discover activity patterns without supervision, the MC-HCRF model, which can explicitly investigate certainty in latent temporal patterns, and the FuzzySR model, which is used to segment continuous data into events and probabilistically recognize human activities. Cognition models based on recognition results are also implemented for decision making that allow robotic systems to react to human activities. Finally, I will conclude with a discussion of future directions that will accelerate the upcoming technological revolution of human-centered robotics

Multigranularity Representations for Human Inter-Actions: Pose, Motion and Intention

Tracking people and their body pose in videos is a central problem in computer vision. Standard tracking representations reason about temporal coherence of detected people and body parts. They have difficulty tracking targets under partial occlusions or rare body poses, where detectors often fail, since the number of training examples is often too small to deal with the exponential variability of such configurations. We propose tracking representations that track and segment people and their body pose in videos by exploiting information at multiple detection and segmentation granularities when available, whole body, parts or point trajectories. Detections and motion estimates provide contradictory information in case of false alarm detections or leaking motion affinities. We consolidate contradictory information via graph steering, an algorithm for simultaneous detection and co-clustering in a two-granularity graph of motion trajectories and detections, that corrects motion leakage between correctly detected objects, while being robust to false alarms or spatially inaccurate detections. We first present a motion segmentation framework that exploits long range motion of point trajectories and large spatial support of image regions. We show resulting video segments adapt to targets under partial occlusions and deformations. Second, we augment motion-based representations with object detection for dealing with motion leakage. We demonstrate how to combine dense optical flow trajectory affinities with repulsions from confident detections to reach a global consensus of detection and tracking in crowded scenes. Third, we study human motion and pose estimation. We segment hard to detect, fast moving body limbs from their surrounding clutter and match them against pose exemplars to detect body pose under fast motion. We employ on-the-fly human body kinematics to improve tracking of body joints under wide deformations. We use motion segmentability of body parts for re-ranking a set of body joint candidate trajectories and jointly infer multi-frame body pose and video segmentation. We show empirically that such multi-granularity tracking representation is worthwhile, obtaining significantly more accurate multi-object tracking and detailed body pose estimation in popular datasets

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

Important Person Detection from Multiple Videos

Given a crowd-sourced set of videos of a crowded public event, this thesis addresses the problem of detecting and grouping appearances of every person in the scenes. The persons are ranked according to the amount of their occurrence. The rank of a person is considered as the measure of his/her importance. Grouping appearances of every individual from such videos is a very challenging task. This is due to unavailability of prior information or training data, large changes in illumination, huge variations in camera viewpoints, severe occlusions and videos from different photographers. These problems are made tractable by exploiting a variety of visual and contextual cues – appearance, sensor data and co-occurrence of people. This thesis provides a unified framework that integrates these cues to establish an efficient person matching process across videos of the same event. The presence of a person is detected based on a multi-view face detector followed by an efficient person tracking that tracks the detected persons in remaining video frames. The performance of person tracker is optimized by utilizing two independent trackers; one for the face and the other for clothes, and the clothes are detected by taking a bounding box below the face region. The person matching is performed using the facial appearance (biometric) and colors of clothes (non-biometric). Unlike traditional matching algorithms that use only low-level facial features for face identification, high-level attribute classifiers (i.e., Gender, ethnicity, hair color, etc.) are also utilized to enhance the identification performance. Hierarchical Agglomerative Clustering (HAC) is used to group the individuals within a video and also across videos. The performance of HAC is improved by using contextual constraints, such as a person cannot appear twice in the same frame. These constraints are directly enforced by altering the HAC algorithm. Finally the detected individuals are ranked according to the number of videos in which they appear and ‘N’ top ranked individuals are taken as important persons. The performance of the proposed algorithm is validated on two novel challenging datasets. The contribution of this thesis is twofold. First, a unified framework is proposed that does not require any prior information or training data about the individuals. The framework is completely automatic and does not require any human interaction. Second, we demonstrate how usage of multiple visual modalities and contextual cues can be exploited to enhance the performance of persons matching under real life problems. Experimental results show the effectiveness of the framework and ensure that the proposed system provides competitive results with the state-of-art algorithms

Stochastic optimization and interactive machine learning for human motion analysis

The analysis of human motion from visual data is a central issue in the computer vision research community as it enables a wide range of applications and it still remains a challenging problem when dealing with unconstrained scenarios and general conditions. Human motion analysis is used in the entertainment industry for movies or videogame production, in medical applications for rehabilitation or biomechanical studies. It is also used for human computer interaction in any kind of environment, and moreover, it is used for big data analysis from social networks such as Youtube or Flickr, to mention some of its use cases. In this thesis we have studied human motion analysis techniques with a focus on its application for smart room environments. That is, we have studied methods that will support the analysis of people behavior in the room, allowing interaction with computers in a natural manner and in general, methods that introduce computers in human activity environments to enable new kind of services but in an unobstrusive mode. The thesis is structured in two parts, where we study the problem of 3D pose estimation from multiple views and the recognition of gestures using range sensors. First, we propose a generic framework for hierarchically layered particle filtering (HPF) specially suited for motion capture tasks. Human motion capture problem generally involve tracking or optimization of high-dimensional state vectors where also one have to deal with multi-modal pdfs. HPF allow to overcome the problem by means of multiple passes through substate space variables. Then, based on the HPF framework, we propose a method to estimate the anthropometry of the subject, which at the end allows to obtain a human body model adjusted to the subject. Moreover, we introduce a new weighting function strategy for approximate partitioning of observations and a method that employs body part detections to improve particle propagation and weight evaluation, both integrated within the HPF framework. The second part of this thesis is centered in the detection of gestures, and we have focused the problem of reducing annotation and training efforts required to train a specific gesture. In order to reduce the efforts required to train a gesture detector, we propose a solution based on online random forests that allows training in real-time, while receiving new data in sequence. The main aspect that makes the solution effective is the method we propose to collect the hard negatives examples while training the forests. The method uses the detector trained up to the current frame to test on that frame, and then collects samples based on the response of the detector such that they will be more relevant for training. In this manner, training is more effective in terms of the number of annotated frames required.L'anàlisi del moviment humà a partir de dades visuals és un tema central en la recerca en visió per computador, per una banda perquè habilita un ampli espectre d'aplicacions i per altra perquè encara és un problema no resolt quan és aplicat en escenaris no controlats. L'analisi del moviment humà s'utilitza a l'indústria de l'entreteniment per la producció de pel·lícules i videojocs, en aplicacions mèdiques per rehabilitació o per estudis bio-mecànics. També s'utilitza en el camp de la interacció amb computadors o també per l'analisi de grans volums de dades de xarxes socials com Youtube o Flickr, per mencionar alguns exemples. En aquesta tesi s'han estudiat tècniques per l'anàlisi de moviment humà enfocant la seva aplicació en entorns de sales intel·ligents. És a dir, s'ha enfocat a mètodes que puguin permetre l'anàlisi del comportament de les persones a la sala, que permetin la interacció amb els dispositius d'una manera natural i, en general, mètodes que incorporin les computadores en espais on hi ha activitat de persones, per habilitar nous serveis de manera que no interfereixin en la activitat. A la primera part, es proposa un marc genèric per l'ús de filtres de partícules jeràrquics (HPF) especialment adequat per tasques de captura de moviment humà. La captura de moviment humà generalment implica seguiment i optimització de vectors d'estat de molt alta dimensió on a la vegada també s'han de tractar pdf's multi-modals. Els HPF permeten tractar aquest problema mitjançant multiples passades en subdivisions del vector d'estat. Basant-nos en el marc dels HPF, es proposa un mètode per estimar l'antropometria del subjecte, que a la vegada permet obtenir un model acurat del subjecte. També proposem dos nous mètodes per la captura de moviment humà. Per una banda, el APO es basa en una nova estratègia per les funcions de cost basada en la partició de les observacions. Per altra, el DD-HPF utilitza deteccions de parts del cos per millorar la propagació de partícules i l'avaluació de pesos. Ambdós mètodes són integrats dins el marc dels HPF. La segona part de la tesi es centra en la detecció de gestos, i s'ha enfocat en el problema de reduir els esforços d'anotació i entrenament requerits per entrenar un detector per un gest concret. Per tal de reduir els esforços requerits per entrenar un detector de gestos, proposem una solució basada en online random forests que permet l'entrenament en temps real, mentre es reben noves dades sequencialment. El principal aspecte que fa la solució efectiva és el mètode que proposem per obtenir mostres negatives rellevants, mentre s'entrenen els arbres de decisió. El mètode utilitza el detector entrenat fins al moment per recollir mostres basades en la resposta del detector, de manera que siguin més rellevants per l'entrenament. D'aquesta manera l'entrenament és més efectiu pel que fa al nombre de mostres anotades que es requereixen

Articulated people detection and pose estimation in challenging real world environments

In this thesis we are interested in the problem of articulated people detection and pose estimation being key ingredients towards understanding visual scenes containing people. First, we investigate how statistical 3D human shape models from computer graphics can be leveraged to ease training data generation. Second, we develop expressive models for 2D single- and multi-person pose estimation. Third, we introduce a novel human pose estimation benchmark that makes a significant advance in terms of diversity and difficulty. Thorough experimental evaluation on standard benchmarks demonstrates significant improvements due to the proposed data augmentation techniques and novel body models, while detailed performance analysis of competing approaches on our novel benchmark allows to identify the most promising directions of improvement.In dieser Arbeit untersuchen wir das Problem der artikulierten Detektion und Posenschätzung von Personen als Schlüsselkomponenten des Verstehens von visuellen Szenen mit Personen. Obwohl es umfangreiche Bemühungen gibt, die Lösung dieser Probleme anzugehen, haben wir drei vielversprechende Herangehensweisen ermittelt, die unserer Meinung nach bisher nicht ausreichend beachtet wurden. Erstens untersuchen wir, wie statistische 3 D Modelle des menschlichen Umrisses, die aus der Computergrafik stammen, wirksam eingesetzt werden können, um die Generierung von Trainingsdaten zu erleichtern. Wir schlagen eine Reihe von Techniken zur automatischen Datengenerierung vor, die eine direkte Repräsentation relevanter Variationen in den Trainingsdaten erlauben. Indem wir Stichproben aus der zu Grunde liegenden Verteilung des menschlichen Umrisses und aus einem großen Datensatz von menschlichen Posen ziehen, erzeugen wir eine neue für unsere Aufgabe relevante Auswahl mit regulierbaren Variationen von Form und Posen. Darüber hinaus verbessern wir das neueste 3 D Modell des menschlichen Umrisses selbst, indem wir es aus einem großen handelsüblichen Datensatz von 3 D Körpern neu aufbauen. Zweitens entwickeln wir ausdrucksstarke räumliche Modelle und ErscheinungsbildModelle für die 2 D Posenschätzung einzelner und mehrerer Personen. Wir schlagen ein ausdrucksstarkes Einzelperson-Modell vor, das Teilabhängigkeiten höherer Ordnung einbezieht, aber dennoch effizient bleibt. Wir verstärken dieses Modell durch verschiedene Arten von starken Erscheinungsbild-Repräsentationen, um die Körperteilhypothesen erheblich zu verbessern. Schließlich schlagen wir ein ausdruckstarkes Modell zur gemeinsamen Posenschätzung mehrerer Personen vor. Dazu entwickeln wir starke Deep Learning-basierte Körperteildetektoren und ein ausdrucksstarkes voll verbundenes räumliches Modell. Der vorgeschlagene Ansatz behandelt die Posenschätzung mehrerer Personen als ein Problem der gemeinsamen Aufteilung und Annotierung eines Satzes von Körperteilhypothesen: er erschließt die Anzahl von Personen in einer Szene, identifiziert verdeckte Körperteile und unterscheidet eindeutig Körperteile von Personen, die sich nahe beieinander befinden. Drittens führen wir eine gründliche Bewertung und Performanzanalyse führender Methoden der menschlichen Posenschätzung und Aktivitätserkennung durch. Dazu stellen wir einen neuen Benchmark vor, der einen bedeutenden Fortschritt bezüglich Diversität und Schwierigkeit im Vergleich zu bisherigen Datensätzen mit sich bringt und über 40 . 000 annotierte Körperposen und mehr als 1 . 5 Millionen Einzelbilder enthält. Darüber hinaus stellen wir einen reichhaltigen Satz an Annotierungen zur Verfügung, die zu einer detaillierten Analyse konkurrierender Herangehensweisen benutzt werden, wodurch wir Erkenntnisse zu Erfolg und Mißerfolg dieser Methoden erhalten. Zusammengefasst präsentiert diese Arbeit einen neuen Ansatz zur artikulierten Detektion und Posenschätzung von Personen. Eine gründliche experimentelle Evaluation auf Standard-Benchmarkdatensätzen zeigt signifikante Verbesserungen durch die vorgeschlagenen Datenverstärkungstechniken und neuen Körpermodelle, während eine detaillierte Performanzanalyse konkurrierender Herangehensweisen auf unserem neu vorgestellten großen Benchmark uns erlaubt, die vielversprechendsten Bereiche für Verbesserungen zu erkennen

Human Pose Estimation with Supervoxels

This thesis investigates how segmentation as a preprocessing step can reduce both the search space as well as complexity of human pose estimation in the context of smart environments. A 3D reconstruction is computed with a voxel carving algorithm. Based on a superpixel algorithm, these voxels are segmented into supervoxels that are then applied to pictorial structures in 3D to efficiently estimate the human pose. Both static and dynamic gesture recognition applications were developed

Deep Learning-Based Robotic Perception for Adaptive Facility Disinfection

Hospitals, schools, airports, and other environments built for mass gatherings can become hot spots for microbial pathogen colonization, transmission, and exposure, greatly accelerating the spread of infectious diseases across communities, cities, nations, and the world. Outbreaks of infectious diseases impose huge burdens on our society. Mitigating the spread of infectious pathogens within mass-gathering facilities requires routine cleaning and disinfection, which are primarily performed by cleaning staff under current practice. However, manual disinfection is limited in terms of both effectiveness and efficiency, as it is labor-intensive, time-consuming, and health-undermining. While existing studies have developed a variety of robotic systems for disinfecting contaminated surfaces, those systems are not adequate for intelligent, precise, and environmentally adaptive disinfection. They are also difficult to deploy in mass-gathering infrastructure facilities, given the high volume of occupants. Therefore, there is a critical need to develop an adaptive robot system capable of complete and efficient indoor disinfection. The overarching goal of this research is to develop an artificial intelligence (AI)-enabled robotic system that adapts to ambient environments and social contexts for precise and efficient disinfection. This would maintain environmental hygiene and health, reduce unnecessary labor costs for cleaning, and mitigate opportunity costs incurred from infections. To these ends, this dissertation first develops a multi-classifier decision fusion method, which integrates scene graph and visual information, in order to recognize patterns in human activity in infrastructure facilities. Next, a deep-learning-based method is proposed for detecting and classifying indoor objects, and a new mechanism is developed to map detected objects in 3D maps. A novel framework is then developed to detect and segment object affordance and to project them into a 3D semantic map for precise disinfection. Subsequently, a novel deep-learning network, which integrates multi-scale features and multi-level features, and an encoder network are developed to recognize the materials of surfaces requiring disinfection. Finally, a novel computational method is developed to link the recognition of object surface information to robot disinfection actions with optimal disinfection parameters

Human Pose Estimation with Supervoxels

This thesis investigates how segmentation as a preprocessing step can reduce both the search space as well as complexity of human pose estimation in the context of smart environments. A 3D reconstruction is computed with a voxel carving algorithm. Based on a superpixel algorithm, these voxels are segmented into supervoxels that are then applied to pictorial structures in 3D to efficiently estimate the human pose. Both static and dynamic gesture recognition applications were developed