27 research outputs found

    Investigating keyframe selection methods in the novel domain of passively captured visual lifelogs

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    The SenseCam is a passive capture wearable camera, worn around the neck, and when worn continuously it takes an average of 1,900 images per day. It can be used to create a personal lifelog or visual recording of the wearer’s life which can be helpful as an aid to human memory. For such a large amount of visual information to be useful, it needs to be structured into “events”, which can be achieved through automatic segmentation. An important component of this structuring process is the selection of keyframes to represent individual events. This work investigates a variety of techniques for the selection of a single representative keyframe image from each event, in order to provide the user with an instant visual summary of that event. In our experiments we use a large test set of 2,232 lifelog events collected by 5 users over a time period of one month each. We propose a novel keyframe selection technique which seeks to select the image with the highest “quality” as the keyframe. The inclusion of “quality” approaches in keyframe selection is demonstrated to be useful owing to the high variability in image visual quality within passively captured image collections

    Keyframe detection in visual lifelogs

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    The SenseCam is a wearable camera that passively captures images. Therefore, it requires no conscious effort by a user in taking a photo. A Visual Diary from such a source could prove to be a valuable tool in assisting the elderly, individuals with neurodegenerative diseases, or other traumas. One issue with Visual Lifelogs is the large volume of image data generated. In previous work we spit a day's worth of images into more manageable segments, i.e. into distinct events or activities. However, each event coud stil consist of 80-100 images. thus, in this paper we propose a novel approach to selecting the key images within an event using a combination of MPEG-7 and Scale Invariant Feature Transform (SIFT) features

    Multiple multimodal mobile devices: Lessons learned from engineering lifelog solutions

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    For lifelogging, or the recording of one’s life history through digital means, to be successful, a range of separate multimodal mobile devices must be employed. These include smartphones such as the N95, the Microsoft SenseCam – a wearable passive photo capture device, or wearable biometric devices. Each collects a facet of the bigger picture, through, for example, personal digital photos, mobile messages and documents access history, but unfortunately, they operate independently and unaware of each other. This creates significant challenges for the practical application of these devices, the use and integration of their data and their operation by a user. In this chapter we discuss the software engineering challenges and their implications for individuals working on integration of data from multiple ubiquitous mobile devices drawing on our experiences working with such technology over the past several years for the development of integrated personal lifelogs. The chapter serves as an engineering guide to those considering working in the domain of lifelogging and more generally to those working with multiple multimodal devices and integration of their data

    Information access tasks and evaluation for personal lifelogs

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    Emerging personal lifelog (PL) collections contain permanent digital records of information associated with individuals’ daily lives. This can include materials such as emails received and sent, web content and other documents with which they have interacted, photographs, videos and music experienced passively or created, logs of phone calls and text messages, and also personal and contextual data such as location (e.g. via GPS sensors), persons and objects present (e.g. via Bluetooth) and physiological state (e.g. via biometric sensors). PLs can be collected by individuals over very extended periods, potentially running to many years. Such archives have many potential applications including helping individuals recover partial forgotten information, sharing experiences with friends or family, telling the story of one’s life, clinical applications for the memory impaired, and fundamental psychological investigations of memory. The Centre for Digital Video Processing (CDVP) at Dublin City University is currently engaged in the collection and exploration of applications of large PLs. We are collecting rich archives of daily life including textual and visual materials, and contextual context data. An important part of this work is to consider how the effectiveness of our ideas can be measured in terms of metrics and experimental design. While these studies have considerable similarity with traditional evaluation activities in areas such as information retrieval and summarization, the characteristics of PLs mean that new challenges and questions emerge. We are currently exploring the issues through a series of pilot studies and questionnaires. Our initial results indicate that there are many research questions to be explored and that the relationships between personal memory, context and content for these tasks is complex and fascinating

    Validating the detection of everyday concepts in visual lifelogs

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    The Microsoft SenseCam is a small lightweight wearable camera used to passively capture photos and other sensor readings from a user's day-to-day activities. It can capture up to 3,000 images per day, equating to almost 1 million images per year. It is used to aid memory by creating a personal multimedia lifelog, or visual recording of the wearer's life. However the sheer volume of image data captured within a visual lifelog creates a number of challenges, particularly for locating relevant content. Within this work, we explore the applicability of semantic concept detection, a method often used within video retrieval, on the novel domain of visual lifelogs. A concept detector models the correspondence between low-level visual features and high-level semantic concepts (such as indoors, outdoors, people, buildings, etc.) using supervised machine learning. By doing so it determines the probability of a concept's presence. We apply detection of 27 everyday semantic concepts on a lifelog collection composed of 257,518 SenseCam images from 5 users. The results were then evaluated on a subset of 95,907 images, to determine the precision for detection of each semantic concept and to draw some interesting inferences on the lifestyles of those 5 users. We additionally present future applications of concept detection within the domain of lifelogging. © 2008 Springer Berlin Heidelberg

    VRLE: Lifelog Interaction Prototype in Virtual Reality:Lifelog Search Challenge at ACM ICMR 2020

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    The Lifelog Search Challenge (LSC) invites researchers to share their prototypes for interactive lifelog retrieval and encourages competition to develop and evaluate effective methodologies to achieve this. With this paper we present a novel approach to visual lifelog exploration based on our research to date utilising virtual reality as a medium for interactive information retrieval. The VRLE prototype presented is an iteration on a previous system which won the first LSC competition at ACM ICMR 2018

    Providing effective memory retrieval cues through automatic structuring and augmentation of a lifelog of images

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    Lifelogging is an area of research which is concerned with the capture of many aspects of an individual's life digitally, and within this rapidly emerging field is the significant challenge of managing images passively captured by an individual of their daily life. Possible applications vary from helping those with neurodegenerative conditions recall events from memory, to the maintenance and augmentation of extensive image collections of a tourist's trips. However, a large lifelog of images can quickly amass, with an average of 700,000 images captured each year, using a device such as the SenseCam. We address the problem of managing this vast collection of personal images by investigating automatic techniques that: 1. Identify distinct events within a full day of lifelog images (which typically consists of 2,000 images) e.g. breakfast, working on PC, meeting, etc. 2. Find similar events to a given event in a person's lifelog e.g. "show me other events where I was in the park" 3. Determine those events that are more important or unusual to the user and also select a relevant keyframe image for visual display of an event e.g. a "meeting" is more interesting to review than "working on PC" 4. Augment the images from a wearable camera with higher quality images from external "Web 2.0" sources e.g. find me pictures taken by others of the U2 concert in Croke Park In this dissertation we discuss novel techniques to realise each of these facets and how effective they are. The significance of this work is not only of benefit to the lifelogging community, but also to cognitive psychology researchers studying the potential benefits of lifelogging devices to those with neurodegenerative diseases

    Experiences of aiding autobiographical memory Using the SenseCam

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    Human memory is a dynamic system that makes accessible certain memories of events based on a hierarchy of information, arguably driven by personal significance. Not all events are remembered, but those that are tend to be more psychologically relevant. In contrast, lifelogging is the process of automatically recording aspects of one's life in digital form without loss of information. In this article we share our experiences in designing computer-based solutions to assist people review their visual lifelogs and address this contrast. The technical basis for our work is automatically segmenting visual lifelogs into events, allowing event similarity and event importance to be computed, ideas that are motivated by cognitive science considerations of how human memory works and can be assisted. Our work has been based on visual lifelogs gathered by dozens of people, some of them with collections spanning multiple years. In this review article we summarize a series of studies that have led to the development of a browser that is based on human memory systems and discuss the inherent tension in storing large amounts of data but making the most relevant material the most accessible

    Experiences of aiding autobiographical memory using the sensecam

    Get PDF
    Human memory is a dynamic system that makes accessible certain memories of events based on a hierarchy of information, arguably driven by personal significance. Not all events are remembered, but those that are tend to be more psychologically relevant. In contrast, lifelogging is the process of automatically recording aspects of one's life in digital form without loss of information. In this article we share our experiences in designing computer-based solutions to assist people review their visual lifelogs and address this contrast. The technical basis for our work is automatically segmenting visual lifelogs into events, allowing event similarity and event importance to be computed, ideas that are motivated by cognitive science considerations of how human memory works and can be assisted. Our work has been based on visual lifelogs gathered by dozens of people, some of them with collections spanning multiple years. In this review article we summarize a series of studies that have led to the development of a browser that is based on human memory systems and discuss the inherent tension in storing large amounts of data but making the most relevant material the most accessible
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