Multiple path prediction for traffic scenes using LSTMs and mixture density models

This work presents an analysis of predicting multiple future paths of moving objects in traffic scenes by leveraging Long Short-Term Memory architectures (LSTMs) and Mixture Density Networks (MDNs) in a single-shot manner. Path prediction allows estimating the future positions of objects. This is useful in important applications such as security monitoring systems, Autonomous Driver Assistance Systems and assistive technologies. Normal approaches use observed positions (tracklets) of objects in video frames to predict their future paths as a sequence of position values. This can be treated as a time series. LSTMs have achieved good performance when dealing with time series. However, LSTMs have the limitation of only predicting a single path per tracklet. Path prediction is not a deterministic task and requires predicting with a level of uncertainty. Predicting multiple paths instead of a single one is therefore a more realistic manner of approaching this task. In this work, predicting a set of future paths with associated uncertainty was archived by combining LSTMs and MDNs. The evaluation was made on the KITTI and the CityFlow datasets on three type of objects, four prediction horizons and two different points of view (image coordinates and birds-eye vie

Development of an evidence-based toolkit to support safe design for children

This thesis contributes to developing an evidence-based toolkit for designers when designing products based on theoretical inputs from human-factors study. Theoretical and developmental knowledge, relevant to the design of warning and risk communications and the area of design for child safety, is translated into support for reflections to practitioners. The risk management framework derived from this study aims to increase awareness of the implications of the aspects involved and as a reference point for groups involved in design for child safety. The thesis covers a shift from risk communications with children to information about children (including physical dimensions etc.) for designers. The final output is a collation of this knowledge base and some conceptual tools that can be applied to a specific design context whether that context be in risk communications or the area of general safety design considerations. Designers with little experience in managing design for children can benefit from this study when deciding on their design strategies. This reflective support is the result of a study of risk communication as a complex and unique activity in which various groups and domains are involved. The process of building an understanding started with an analysis of the literature in the field and with the direct experience of the researcher, who worked directly within ergonomics as part of a design-innovation team. The framework presented in this thesis follows a more structured approach to risk communications. It is conceived as an aid to help practitioners reflect on the implications each stage of the development process has on the experience of developing appropriate risk communications and appropriate products. In this way, it is thought of as a dynamic and flexible reference that can be adapted by design researchers when planning and coordinating design to suit different design situations. The use of this tool in the childsafety, design, and study communities would provide validation of the effectiveness of the framework and its continuous improvement. The purpose of this study is twofold: to contribute to study and practice with the aim of providing fundamental guidance to designers. The research detailed in this thesis brings readers up-to-date with the current literature on theories of risk communications. It then highlights methodologies, tools, guidelines and requirements for risk communication advances in study and practice. A framework for risk communication for young children has been developed out of a resource review based on previous work in the area by McLaughlin and Mayhorn, (2014). The information accumulated in this study has been used to develop initial prototype tools for designers who are considering young children. The developed platform supports practitioners from two different angles: theoretical and practical. Designers engaged in the core activity of design for child safety need methods that support the consideration of ergonomics and other product requirements, such as risk communications. This study contributes to developing methods and tools that can be used by designers and other relevant groups when designing risk communications for young children. Available knowledge is collated and integrated into the framework with the intention that it will be developed further throughout the thesis to consider effective use within the design cycle. This study aims to contribute to child safety by providing the first development of tools/decision supports aimed at designers who are designing for young children and are accordingly evaluating human-factors methods in design for child safety. The aim of this study is to gather the requirements of a collaborative design tool for use by industrial designers, engineers and other groups involved in design for child safety. This thesis aims to address these needs. When considering the needs, limitations and capabilities, ( mental model ) of the intended users (i.e., children), important aspects such as safety have been considered. The general need for support methods are addressed through a review of the safety, design and ergonomics literature. After this, empirical study through interviews and observations is used to outline some problem areas: the development and implementation of human-factors methods in design, lack of available resources and inaccessibility of data. Three empirical studies were conducted to meet the requirements of this study: Study 1 in Chapter 4 involves documentary analysis of existing models and methods, Study 2 in Chapter 5 involves formal interviews with designers (N = 30), and Study 3 in Chapter 7 involves an online survey for initial feedback about the prototype-persona (N = 50) respondents. The first section outlines the study questions. It discusses the outputs of the three main studies contained within this thesis

People, Penguins and Petri Dishes: Adapting Object Counting Models To New Visual Domains And Object Types Without Forgetting

In this paper we propose a technique to adapt a convolutional neural network (CNN) based object counter to additional visual domains and object types while still preserving the original counting function. Domain-specific normalisation and scaling operators are trained to allow the model to adjust to the statistical distributions of the various visual domains. The developed adaptation technique is used to produce a singular patch-based counting regressor capable of counting various object types including people, vehicles, cell nuclei and wildlife. As part of this study a challenging new cell counting dataset in the context of tissue culture and patient diagnosis is constructed. This new collection, referred to as the Dublin Cell Counting (DCC) dataset, is the first of its kind to be made available to the wider computer vision community. State-of-the-art object counting performance is achieved in both the Shanghaitech (parts A and B) and Penguins datasets while competitive performance is observed on the TRANCOS and Modified Bone Marrow (MBM) datasets, all using a shared counting model.Comment: 10 page

ResnetCrowd: a residual deep learning architecture for crowd counting, violent behaviour detection and crowd density level classification

In this paper we propose ResnetCrowd, a deep residual architecture for simultaneous crowd counting, violent behaviour detection and crowd density level classification. To train and evaluate the proposed multi-objective technique, a new 100 image dataset referred to as Multi Task Crowd is constructed. This new dataset is the first computer vision dataset fully annotated for crowd counting, violent behaviour detection and density level classification. Our experiments show that a multi-task approach boosts individual task performance for all tasks and most notably for violent behaviour detection which receives a 9\% boost in ROC curve AUC (Area under the curve). The trained ResnetCrowd model is also evaluated on several additional benchmarks highlighting the superior generalisation of crowd analysis models trained for multiple objectives

Action recognition based on sparse motion trajectories

We present a method that extracts effective features in videos for human action recognition. The proposed method analyses the 3D volumes along the sparse motion trajectories of a set of interest points from the video scene. To represent human actions, we generate a Bag-of-Features (BoF) model based on extracted features, and finally a support vector machine is used to classify human activities. Evaluation shows that the proposed features are discriminative and computationally efficient. Our method achieves state-of-the-art performance with the standard human action recognition benchmarks, namely KTH and Weizmann datasets

Action recognition in video using a spatial-temporal graph-based feature representation

We propose a video graph based human action recognition framework. Given an input video sequence, we extract spatio-temporal local features and construct a video graph to incorporate appearance and motion constraints to reflect the spatio-temporal dependencies among features. them. In particular, we extend a popular dbscan density-based clustering algorithm to form an intuitive video graph. During training, we estimate a linear SVM classifier using the standard Bag-of-words method. During classification, we apply Graph-Cut optimization to find the most frequent action label in the constructed graph and assign this label to the test video sequence. The proposed approach achieves stateof-the-art performance with standard human action recognition benchmarks, namely KTH and UCF-sports datasets and competitive results for the Hollywood (HOHA) dataset

An evaluation of local action descriptors for human action classification in the presence of occlusion

This paper examines the impact that the choice of local de- scriptor has on human action classifier performance in the presence of static occlusion. This question is important when applying human action classification to surveillance video that is noisy, crowded, complex and incomplete. In real-world scenarios, it is natural that a human can be occluded by an object while carrying out different actions. However, it is unclear how the performance of the proposed action descriptors are affected by the associated loss of information. In this paper, we evaluate and compare the classification performance of the state-of-art human local action descriptors in the presence of varying degrees of static occlusion. We consider four different local action descriptors: Trajectory (TRAJ), Histogram of Orientation Gradient (HOG), Histogram of Orientation Flow (HOF) and Motion Boundary Histogram (MBH). These descriptors are combined with a standard bag-of-features representation and a Support Vector Machine classifier for action recognition. We investigate the performance of these descriptors and their possible combinations with respect to varying amounts of artificial occlusion in the KTH action dataset. This preliminary investigation shows that MBH in combination with TRAJ has the best performance in the case of partial occlusion while TRAJ in combination with MBH achieves the best results in the presence of heavy occlusion

How blogs support the transfer of knowledge into practice in the field of dementia palliative care: a survey of facilitators and barriers

BACKGROUND: Blogging can help to maximise the impact of one’s work in academia and beyond by making research findings accessible for multiple knowledge users, such as healthcare professionals and the public, as well as other researchers. As part of the knowledge exchange and dissemination activities of the Model for Dementia Palliative Care Project, this study explored stakeholders’ views of blogs as a means to translate research findings. METHODS: A web-based survey was developed, piloted, and revised. It was distributed electronically via key dementia and palliative care organisations websites, newsletters, social media platforms, and within the staff mailing lists of five Universities in Ireland. Data were analysed using descriptive statistics and content analysis. RESULTS: Complete responses were received from 128 participants. The majority of respondents were healthcare researchers (n = 53), followed by healthcare providers (n = 46). The preferred methods of reviewing research findings were scientific papers, websites and news articles. Respondents read healthcare blogs “sometimes” (39.1%), with < 19% reading them “often” or “very often”. Receiving an email notification might increase the likelihood of reading a new blog post for 83% of respondents. Barriers to engaging with blogs included lack of time, preference for other media, lack of awareness regarding available blogs, and concerns about the credibility and source of information. An appropriate length and the author of the blog were key features that encouraged engagement with a blog. CONCLUSIONS: Despite respondents choosing a scientific paper as their preferred method to consume research findings, many indicated an openness to reading blogs on their area of interest. Creating concise, relevant, and credible blogs, and suitably promoting them, could increase the impact and reach of healthcare research, such as in the emerging field of dementia palliative care, and thus promote translation of research findings into practice. SUPPLEMENTARY INFORMATION: The online version contains supplementary material available at 10.1186/s12904-022-01001-7

A scoping review of the evidence for community-based dementia palliative care services and their related service activities

BACKGROUND: Palliative care is identified internationally as a priority for efficacious dementia care. Research into “effective models” of palliative care for people with dementia has been recommended by several European countries. To build an effective service-delivery model we must gain an understanding of existing models used in similar settings. The study aim is to identify core components of extant models of palliative care for people with dementia, and their families, who are living at home in the community. METHODS: A scoping review was employed. The search strategy was devised to identify all peer-reviewed research papers relating to the above aim. This process was iterative, and the search strategy was refined as evidence emerged and was reviewed. All types of study designs and both quantitative and qualitative studies of non-pharmacological interventions were considered for inclusion. RESULTS: The search identified 2,754 unique citations, of which 18 papers were deemed eligible for inclusion. Although a palliative care approach is recommended from early in the disease process, most evidence involves end-of-life care or advanced dementia and pertains to residential care. The majority of the research reviewed focused on the effects of advance care planning, and end-of-life care; specialist palliative care input, and/or generalist palliative care provided by dementia services to enable people to remain at home and to reduce costs of care. Community staff training in palliative care appeared to improve engagement with Specialist Palliative Care teams. Integration of dementia and palliative care services was found to improve care received for people with dementia and their carers. CONCLUSIONS: While the evidence for integration of dementia and palliative care services is promising, further high-quality research is necessary particularly to identify the key components of palliative care for people living with dementia. This is imperative to enable people with dementia to inform their own care, to stay living at home for as long as possible, and, where appropriate, to die at home. SUPPLEMENTARY INFORMATION: The online version contains supplementary material available at 10.1186/s12904-022-00922-7