Temporal saliency adaptation in egocentric videos

This work adapts a deep neural model for image saliency prediction to the temporal domain of egocentric video. We compute the saliency map for each video frame, firstly with an off-the-shelf model trained from static images, secondly by adding a a convolutional or conv-LSTM layers trained with a dataset for video saliency prediction. We study each configuration on EgoMon, a new dataset made of seven egocentric videos recorded by three subjects in both free-viewing and task-driven set ups. Our results indicate that the temporal adaptation is beneficial when the viewer is not moving and observing the scene from a narrow field of view. Encouraged by this observation, we compute and publish the saliency maps for the EPIC Kitchens dataset, in which view- ers are cooking

Multimodal Egocentric Analysis of Focused Interactions

Continuous detection of social interactions from wearable sensor data streams has a range of potential applications in domains, including health and social care, security, and assistive technology. We contribute an annotated, multimodal data set capturing such interactions using video, audio, GPS, and inertial sensing. We present methods for automatic detection and temporal segmentation of focused interactions using support vector machines and recurrent neural networks with features extracted from both audio and video streams. The focused interaction occurs when the co-present individuals, having the mutual focus of attention, interact by first establishing the face-to-face engagement and direct conversation. We describe an evaluation protocol, including framewise, extended framewise, and event-based measures, and provide empirical evidence that the fusion of visual face track scores with audio voice activity scores provides an effective combination. The methods, contributed data set, and protocol together provide a benchmark for the future research on this problem

A hybrid egocentric video summarization method to improve the healthcare for Alzheimer patients

Alzheimer patients face difficulty to remember the identity of persons and performing daily life activities. This paper presents a hybrid method to generate the egocentric video summary of important people, objects and medicines to facilitate the Alzheimer patients to recall their deserted memories. Lifelogging video data analysis is used to recall the human memory; however, the massive amount of lifelogging data makes it a challenging task to select the most relevant content to educate the Alzheimer’s patient. To address the challenges associated with massive lifelogging content, static video summarization approach is applied to select the key-frames that are more relevant in the context of recalling the deserted memories of the Alzheimer patients. This paper consists of three main modules that are face, object, and medicine recognition. Histogram of oriented gradient features are used to train the multi-class SVM for face recognition. SURF descriptors are employed to extract the features from the input video frames that are then used to find the corresponding points between the objects in the input video and the reference objects stored in the database. Morphological operators are applied followed by the optical character recognition to recognize and tag the medicines for Alzheimer patients. The performance of the proposed system is evaluated on 18 real-world homemade videos. Experimental results signify the effectiveness of the proposed system in terms of providing the most relevant content to enhance the memory of Alzheimer patients

Rethinking the test collection methodology for personal self-tracking data

While vast volumes of personal data are being gathered daily by individuals, the MMM community has not really been tackling the challenge of developing novel retrieval algorithms for this data, due to the challenges of getting access to the data in the first place. While initial efforts have taken place on a small scale, it is our conjecture that a new evaluation paradigm is required in order to make progress in analysing, modeling and retrieving from personal data archives. In this position paper, we propose a new model of Evaluation-as-a-Service that re-imagines the test collection methodology for personal multimedia data in order to address the many challenges of releasing test collections of personal multimedia data

Towards accurate multi-person pose estimation in the wild

In this thesis we are concerned with the problem of articulated human pose estimation and pose tracking in images and video sequences. Human pose estimation is a task of localising major joints of a human skeleton in natural images and is one of the most important visual recognition tasks in the scenes containing humans with numerous applications in robotics, virtual and augmented reality, gaming and healthcare among others. Articulated human pose tracking requires tracking multiple persons in the video sequence while simultaneously estimating full body poses. This task is important for analysing surveillance footage, activity recognition, sports analytics, etc. Most of the prior work focused on the pose estimation of single pre-localised humans whereas here we address a case with multiple people in real world images which entails several challenges such as person-person overlaps in highly crowded scenes, unknown number of people or people entering and leaving video sequences. The first contribution is a multi-person pose estimation algorithm based on the bottom-up detection-by-grouping paradigm. Unlike the widespread top-down approaches our method detects body joints and pairwise relations between them in a single forward pass of a convolutional neural network. Multi-person parsing is performed by optimizing a joint objective based on a multicut graph partitioning framework. Secondly, we extend our pose estimation approach to articulated multi-person pose tracking in videos. Our approach performs multi-target tracking and pose estimation in a holistic manner by optimising a single objective. We further simplify and refine the formulation which allows us to reach close to the real-time performance. Thirdly, we propose a large scale dataset and a benchmark for articulated multi-person tracking. It is the first dataset of video sequences comprising complex multi-person scenes and fully annotated tracks with 2D keypoints. Our fourth contribution is a method for estimating 3D body pose using on-body wearable cameras. Our approach uses a pair of downward facing, head-mounted cameras and captures an entire body. This egocentric approach is free of limitations of traditional setups with external cameras and can estimate body poses in very crowded environments. Our final contribution goes beyond human pose estimation and is in the field of deep learning of 3D object shapes. In particular, we address the case of reconstructing 3D objects from weak supervision. Our approach represents objects as 3D point clouds and is able to learn them with 2D supervision only and without requiring camera pose information at training time. We design a differentiable renderer of point clouds as well as a novel loss formulation for dealing with camera pose ambiguity.In dieser Arbeit behandeln wir das Problem der Schätzung und Verfolgung artikulierter menschlicher Posen in Bildern und Video-Sequenzen. Die Schätzung menschlicher Posen besteht darin die Hauptgelenke des menschlichen Skeletts in natürlichen Bildern zu lokalisieren und ist eine der wichtigsten Aufgaben der visuellen Erkennung in Szenen, die Menschen beinhalten. Sie hat zahlreiche Anwendungen in der Robotik, virtueller und erweiterter Realität, in Videospielen, in der Medizin und weiteren Bereichen. Die Verfolgung artikulierter menschlicher Posen erfordert die Verfolgung mehrerer Personen in einer Videosequenz bei gleichzeitiger Schätzung vollständiger Körperhaltungen. Diese Aufgabe ist besonders wichtig für die Analyse von Video-Überwachungsaufnahmen, Aktivitätenerkennung, digitale Sportanalyse etc. Die meisten vorherigen Arbeiten sind auf die Schätzung einzelner Posen vorlokalisierter Menschen fokussiert, wohingegen wir den Fall mehrerer Personen in natürlichen Aufnahmen betrachten. Dies bringt einige Herausforderungen mit sich, wie die Überlappung verschiedener Personen in dicht gedrängten Szenen, eine unbekannte Anzahl an Personen oder Personen die das Sichtfeld der Video-Sequenz verlassen oder betreten. Der erste Beitrag ist ein Algorithmus zur Schätzung der Posen mehrerer Personen, welcher auf dem Paradigma der Erkennung durch Gruppierung aufbaut. Im Gegensatz zu den verbreiteten Verfeinerungs-Ansätzen erkennt unsere Methode Körpergelenke and paarweise Beziehungen zwischen ihnen in einer einzelnen Vorwärtsrechnung eines faltenden neuronalen Netzwerkes. Die Gliederung in mehrere Personen erfolgt durch Optimierung einer gemeinsamen Zielfunktion, die auf dem Mehrfachschnitt-Problem in der Graphenzerlegung basiert. Zweitens erweitern wir unseren Ansatz zur Posen-Bestimmung auf das Verfolgen mehrerer Personen und deren Artikulation in Videos. Unser Ansatz führt eine Verfolgung mehrerer Ziele und die Schätzung der zugehörigen Posen in ganzheitlicher Weise durch, indem eine einzelne Zielfunktion optimiert wird. Desweiteren vereinfachen und verfeinern wir die Formulierung, was unsere Methode nah an Echtzeit-Leistung bringt. Drittens schlagen wir einen großen Datensatz und einen Bewertungsmaßstab für die Verfolgung mehrerer artikulierter Personen vor. Dies ist der erste Datensatz der Video-Sequenzen von komplexen Szenen mit mehreren Personen beinhaltet und deren Spuren komplett mit zwei-dimensionalen Markierungen der Schlüsselpunkte versehen sind. Unser vierter Beitrag ist eine Methode zur Schätzung von drei-dimensionalen Körperhaltungen mittels am Körper tragbarer Kameras. Unser Ansatz verwendet ein Paar nach unten gerichteter, am Kopf befestigter Kameras und erfasst den gesamten Körper. Dieser egozentrische Ansatz ist frei von jeglichen Limitierungen traditioneller Konfigurationen mit externen Kameras und kann Körperhaltungen in sehr dicht gedrängten Umgebungen bestimmen. Unser letzter Beitrag geht über die Schätzung menschlicher Posen hinaus in den Bereich des tiefen Lernens der Gestalt von drei-dimensionalen Objekten. Insbesondere befassen wir uns mit dem Fall drei-dimensionale Objekte unter schwacher Überwachung zu rekonstruieren. Unser Ansatz repräsentiert Objekte als drei-dimensionale Punktwolken and ist im Stande diese nur mittels zwei-dimensionaler Überwachung und ohne Informationen über die Kamera-Ausrichtung zur Trainingszeit zu lernen. Wir entwerfen einen differenzierbaren Renderer für Punktwolken sowie eine neue Formulierung um mit uneindeutigen Kamera-Ausrichtungen umzugehen

Embodied Visual Perception Models For Human Behavior Understanding

Many modern applications require extracting the core attributes of human behavior such as a person\u27s attention, intent, or skill level from the visual data. There are two main challenges related to this problem. First, we need models that can represent visual data in terms of object-level cues. Second, we need models that can infer the core behavioral attributes from the visual data. We refer to these two challenges as ``learning to see\u27\u27, and ``seeing to learn\u27\u27 respectively. In this PhD thesis, we have made progress towards addressing both challenges. We tackle the problem of ``learning to see\u27\u27 by developing methods that extract object-level information directly from raw visual data. This includes, two top-down contour detectors, DeepEdge and HfL, which can be used to aid high-level vision tasks such as object detection. Furthermore, we also present two semantic object segmentation methods, Boundary Neural Fields (BNFs), and Convolutional Random Walk Networks (RWNs), which integrate low-level affinity cues into an object segmentation process. We then shift our focus to video-level understanding, and present a Spatiotemporal Sampling Network (STSN), which can be used for video object detection, and discriminative motion feature learning. Afterwards, we transition into the second subproblem of ``seeing to learn\u27\u27, for which we leverage first-person GoPro cameras that record what people see during a particular activity. We aim to infer the core behavior attributes such as a person\u27s attention, intention, and his skill level from such first-person data. To do so, we first propose a concept of action-objects--the objects that capture person\u27s conscious visual (watching a TV) or tactile (taking a cup) interactions. We then introduce two models, EgoNet and Visual-Spatial Network (VSN), which detect action-objects in supervised and unsupervised settings respectively. Afterwards, we focus on a behavior understanding task in a complex basketball activity. We present a method for evaluating players\u27 skill level from their first-person basketball videos, and also a model that predicts a player\u27s future motion trajectory from a single first-person image