Review of Assistive Devices for Electric Powered Wheelchairs Navigation

The decreasing costs of microprocessor systems and increasing range of “Smart Sensors” have led to a boom in Assistive Device Technology. The annual rate of expenditure for mobility related devices has reached $1 billion dollars in the United States alone. The industries current focus is to develop a wider range of Independent Mobility Controllers to allow, even the most severely disabled person, the ability to control an Electric Powered Wheelchair (EPW). Advances in Autonomous Robot Design have led to corresponding improvements in EPW technology. This paper outlines user interfaces and input device technologies used at present to navigate an EPW

Marked object recognition multitouch screen printed touchpad for interactive applications

The market for interactive platforms is rapidly growing, and touchscreens have been incorporated in an increasing number of devices. Thus, the area of smart objects and devices is strongly increasing by adding interactive touch and multimedia content, leading to new uses and capabilities. In this work, a flexible screen printed sensor matrix is fabricated based on silver ink in a polyethylene terephthalate (PET) substrate. Diamond shaped capacitive electrodes coupled with conventional capacitive reading electronics enables fabrication of a highly functional capacitive touchpad, and also allows for the identification of marked objects. For the latter, the capacitive signatures are identified by intersecting points and distances between them. Thus, this work demonstrates the applicability of a low cost method using royalty-free geometries and technologies for the development of flexible multitouch touchpads for the implementation of interactive and object recognition applications.Portuguese Foundation for Science and Technology (FCT) in the framework of the Strategic Funding UID/FIS/04650/2013. The authors thank the FCT for financial support under projects PTDC/EEI-SII/5582/2014 and PTDC/CTM-ENE/5387/2014. P. C., J.O. and V. C. also thank the FCT for the SFRH/BPD/110914/2015, SFRH/BPD/98219/2013 and SFRH/BPD/97739/2013 grants, respectively. Financial support from the Basque Government Industry Department under the ELKARTEK Program is also acknowledged as well as funding by theSpanish Ministry of Economy and Competitiveness (MINECO) through the project MAT2016-76039-C4-3-Rinfo:eu-repo/semantics/publishedVersio

Robust face clustering for real-world data

This thesis has investigated how to cluster a large number of faces within a multi-media corpus in the presence of large session variation. Quality metrics are used to select the best faces to represent a sequence of faces; and session variation modelling improves clustering performance in the presence of wide variations across videos. Findings from this thesis contribute to improving the performance of both face verification systems and the fully automated clustering of faces from a large video corpus

Assessment of the Effects of Road Geometry on Irish Accident Rates and Driving Behaviour

The following thesis documents the work carried out throughout two main experiments. The first experiment analyses driver crash data and extracts from it accidents that may have occurred as a result of road segments with bad geometry. A visualisation and analysis of this crash data is presented to better determine the relationship ‘if any’ between road geometry and accident points. The second experiment then examines driver behaviour on road segments that also contain bad bends using a purpose built driving simulator. This experiment is ‘driver centric’ as it measures behaviour such as eye movement. Both experiments examine contrasting Irish roadways with an aim to better understand the driver when negotiating various geometries. Findings from the crash data analysis initially show the majority of accidents occurring on straight segments of the road types examined. However, when these accident frequencies are normalised against the percentage of road that consist of straights and bends, interesting signals appear on road types that combine sharp bends with higher road speed limits. Results from driver eye behaviour analysis show drivers fixating on regions of the road based on visible geometry or available sight distance. For example, drivers fixate on areas at the road bend while negotiating sharp bends and fixate further on or above the road surface when traversing straight segments

Robust automatic face clustering in news video

Clustering identities in a video is a useful task to aid in video search, annotation and retrieval, and cast identification. However, reliably clustering faces across multiple videos is challenging task due to variations in the appearance of the faces, as videos are captured in an uncontrolled environment. A person's appearance may vary due to session variations including: lighting and background changes, occlusions, changes in expression and make up. In this paper we propose the novel Local Total Variability Modelling (Local TVM) approach to cluster faces across a news video corpus; and incorporate this into a novel two stage video clustering system. We first cluster faces within a single video using colour, spatial and temporal cues; after which we use face track modelling and hierarchical agglomerative clustering to cluster faces across the entire corpus. We compare different face recognition approaches within this framework. Experiments on a news video database show that the Local TVM technique is able effectively model the session variation observed in the data, resulting in improved clustering performance, with much greater computational efficiency than other methods

Quality based frame selection for face clustering in news video

Clustering identities in a broadcast video is a useful task to aid in video annotation and retrieval. Quality based frame selection is a crucial task in video face clustering, to both improve the clustering performance and reduce the computational cost. We present a frame work that selects the highest quality frames available in a video to cluster the face. This frame selection technique is based on low level and high level features (face symmetry, sharpness, contrast and brightness) to select the highest quality facial images available in a face sequence for clustering. We also consider the temporal distribution of the faces to ensure that selected faces are taken at times distributed throughout the sequence. Normalized feature scores are fused and frames with high quality scores are used in a Local Gabor Binary Pattern Histogram Sequence based face clustering system. We present a news video database to evaluate the clustering system performance. Experiments on the newly created news database show that the proposed method selects the best quality face images in the video sequence, resulting in improved clustering performance

Local inter-session variability modelling for object classification

Object classification is plagued by the issue of session variation. Session variation describes any variation that makes one instance of an object look different to another, for instance due to pose or illumination variation. Recent work in the challenging task of face verification has shown that session variability modelling provides a mechanism to overcome some of these limitations. However, for computer vision purposes, it has only been applied in the limited setting of face verification. In this paper we propose a local region based intersession variability (ISV) modelling approach, and apply it to challenging real-world data. We propose a region based session variability modelling approach so that local session variations can be modelled, termed Local ISV. We then demonstrate the efficacy of this technique on a challenging real-world fish image database which includes images taken underwater, providing significant real-world session variations. This Local ISV approach provides a relative performance improvement of, on average, 23% on the challenging MOBIO, Multi-PIE and SCface face databases. It also provides a relative performance improvement of 35% on our challenging fish image dataset

Strength enhancement in concrete confined by spirals

The strength and ductility of the concrete can be enhanced by confinementlt can be achieved in many ways. Using spirals is one of the ways to enhance the strength by confinement The confinement effect in concrete by spirals can be applicable to enhance the load carrying capacity of columns and shear carrying capacity of beams and flat slabs. This effect prevents structures from catastrophic failures during earthquakes. In this research study, experiments were conducted to determine the anchorage depth of the spiral, the shear enhancement in beams due to confinement by spirals and increment in failure load of flat slab panels a when spiral is used as a shear resistor. The actual shear carrying capacity and theoretical shear carrying capacity of the beams were checked using average integration method and discrete method.The experimental results indicated that the shear carrying capacity of the beam was enhanced by 35.7% for 30mm pitch spiral, 26.8% for 45mm pitch spiral and 16.1% for 60mm pitch spiral. The actual shear carrying capacity based on the experimental results matched closer to the value obtained by the average integration method. The failure load of the flat slab panel was increased by 12.3% when spiral was used as shear resistor

Quality based frame selection for video face recognition

Quality based frame selection is a crucial task in video face recognition, to both improve the recognition rate and to reduce the computational cost. In this paper we present a framework that uses a variety of cues (face symmetry, sharpness, contrast, closeness of mouth, brightness and openness of the eye) to select the highest quality facial images available in a video sequence for recognition. Normalized feature scores are fused using a neural network and frames with high quality scores are used in a Local Gabor Binary Pattern Histogram Sequence based face recognition system. Experiments on the Honda/UCSD database shows that the proposed method selects the best quality face images in the video sequence, resulting in improved recognition performance

Automatic Generation and Population of a Graphics-Based Driving Simulator

A stereo image and high-accuracy positional data were used to generate and populate a low-fidelity graphics model. The data were acquired by a simple mobile mapping system. The use of positional data made it possible to generate automatically a sparse model consisting of a road, central road marking, a green area, and a skybox. This allowed for several applications, such as the synchronization of the model with the video and the semiautomatic population of road signs into the model data. An experiment was conducted to evaluate the model and the video as viable sources for behavioral testing of drivers. The correlations between driver speed in response to the model and the video are presented; this presentation allows for an examination of the effect of the fidelity of the driving simulator’s visual cue stream. The study results were used to compare driver speed in a real vehicle with driver speeds in the video and model roads, with correlations of 84.6% (between video and ground truth), 87.3% (between model and ground truth), and 92.8% (between video and model)