6,092 research outputs found
Template co-updating in multi-modal human activity recognition systems
Multi-modal systems are quite common in the context of human activity
recognition; widely used RGB-D sensors (Kinect is the most prominent example)
give access to parallel data streams, typically RGB images, depth data,
skeleton information. The richness of multimodal information has been largely
exploited in many works in the literature, while an analysis of their
effectiveness for incremental template updating has not been investigated so
far. This paper is aimed at defining a general framework for unsupervised
template updating in multi-modal systems, where the different data sources can
provide complementary information, increasing the effectiveness of the updating
procedure and reducing at the same time the probability of incorrect template
modifications
Human Action Recognition and Monitoring in Ambient Assisted Living Environments
Population ageing is set to become one of the most significant challenges of the 21st century, with implications for almost all sectors of society. Especially in developed countries, governments should immediately implement policies and solutions to facilitate the needs of an increasingly older population. Ambient Intelligence (AmI) and in particular the area of Ambient Assisted Living (AAL) offer a feasible response, allowing the creation of human-centric smart environments that are sensitive and responsive to the needs and behaviours of the user.
In such a scenario, understand what a human being is doing, if and how he/she is interacting with specific objects, or whether abnormal situations are occurring is critical.
This thesis is focused on two related research areas of AAL: the development of innovative vision-based techniques for human action recognition and the remote monitoring of users behaviour in smart environments.
The former topic is addressed through different approaches based on data extracted from RGB-D sensors.
A first algorithm exploiting skeleton joints orientations is proposed. This approach is extended through a multi-modal strategy that includes the RGB channel to define a number of temporal images, capable of describing the time evolution of actions.
Finally, the concept of template co-updating concerning action recognition is introduced. Indeed, exploiting different data categories (e.g., skeleton and RGB information) improve the effectiveness of template updating through co-updating techniques.
The action recognition algorithms have been evaluated on CAD-60 and CAD-120, achieving results comparable with the state-of-the-art. Moreover, due to the lack of datasets including skeleton joints orientations, a new benchmark named Office Activity Dataset has been internally acquired and released.
Regarding the second topic addressed, the goal is to provide a detailed implementation strategy concerning a generic Internet of Things monitoring platform that could be used for checking users' behaviour in AmI/AAL contexts
Knowledge Enhanced Notes (KEN)
To aid the creation and through-life support of large complex engineering products, organisations are placing a greater emphasis on constructing complete and accurate records of design activities. Current documentary approaches are not sufficient to capture activities and decisions in their entirety and can lead to organisations revisiting and in some cases reworking design decisions in order to understand previous design episodes. This paper presents an overview of the challenges in creating accurate, re-usable records of synchronous design activities, enhancing the through-life support of engineering products, followed by the development of an information capture software system to address these challenges. The main objectives for the development of the Knowledge Enhanced Notes system are described followed by the techniques chosen to address the objectives, and finally a description of a use-case for the system. Whilst the focus of the KEN System was to aid the creation and through-life support of large complex engineering products through constructing complete and accurate records of design activities, the system is entirely generic in its application to synchronous activities
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Adaptive detection and tracking using multimodal information
This thesis describes work on fusing data from multiple sources of information, and focuses on two main areas: adaptive detection and adaptive object tracking in automated vision scenarios. The work on adaptive object detection explores a new paradigm in dynamic parameter selection, by selecting thresholds for object detection to maximise agreement between pairs of sources. Object tracking, a complementary technique to object detection, is also explored in a multi-source context and an efficient framework for robust tracking, termed the Spatiogram Bank tracker, is proposed as a means to overcome the difficulties of traditional histogram tracking. As well as performing theoretical analysis of the proposed methods, specific example applications are given for both the detection and the tracking aspects, using thermal infrared and visible spectrum video data, as well as other multi-modal information sources
Latent-Class Hough Forests for 3D object detection and pose estimation of rigid objects
In this thesis we propose a novel framework, Latent-Class Hough Forests, for the problem of 3D object detection and pose estimation in heavily cluttered and occluded scenes. Firstly, we adapt the state-of-the-art template-based representation, LINEMOD [34, 36], into a scale-invariant patch descriptor and integrate it into a regression forest using a novel template-based split function. In training, rather than explicitly collecting representative negative samples, our method is trained on positive samples only and we treat the class distributions at the leaf nodes as latent variables. During the inference process we iteratively update these distributions, providing accurate estimation of background clutter and foreground occlusions and thus a better detection rate. Furthermore, as a by-product, the latent class distributions can provide accurate occlusion aware segmentation masks, even in the multi-instance scenario. In addition to an existing public dataset, which contains only single-instance sequences with large amounts of clutter, we have collected a new, more challenging, dataset for multiple-instance detection containing heavy 2D and 3D clutter as well as foreground occlusions. We evaluate the Latent-Class Hough Forest on both of these datasets where we outperform state-of-the art methods.Open Acces
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