A geometrical-based approach to recognise structure of complex interiors

3D modelling of building interiors has gained a lot of interest recently, specifically since the rise of Building Information Modeling (BIM). A number of methods have been developed in the past, however most of them are limited to modelling non-complex interiors. 3D laser scanners are the preferred sensor to collect the 3D data, however the cost of state-of-the-art laser scanners are prohibitive to many. Other types of sensors could also be used to generate the 3D data but they have limitations especially when dealing with clutter and occlusions. This research has developed a platform to produce 3D modelling of building interiors while adapting a low-cost, low-level laser scanner to generate the 3D interior data. The PreSuRe algorithm developed here, which introduces a new pipeline in modelling building interiors, combines both novel methods and adapts existing approaches to produce the 3D modelling of various interiors, from sparse room to complex interiors with non-ideal geometrical structure, highly cluttered and occluded. This approach has successfully reconstructed the structure of interiors, with above 96% accuracy, even with high amount of noise data and clutter. The time taken to produce the resulting model is almost real-time, compared to existing techniques which may take hours to generate the reconstruction. The produced model is also equipped with semantic information which differentiates the model from a regular 3D CAD drawing and can be use to assist professionals and experts in related fields

Automatic generation of a floor plan from a 3D scanned model: Making the Analogue World Digital

The processing of three-dimensional (3D) room models is an area of research undertaken by many academics and hobbyists due to multiple uses derived from the information obtained - such as the generation of a floor plan; an example of bridging the real and digital world. A floor plan is required when an existing room, floor, or building requires alteration. By having the floor plan in the digital domain it allows the user to alter the room via simulation and render the environment in a life-like manner to determine if the alterations will suffice. This is done using Computer Aided Design Software (CAD). Designing a new room or building would be done using CAD software. However, not all building's digital files are readily available or exist - making the creation of a floor plan necessary. The floor plan can created up by a person on pen and paper, or with using software tools and sensors. Commercial systems exist for this task but there are no automated, open-source systems that can do the same. Current research tends to focus on the processing algorithms and not the sensors or methods for capturing the environment. This dissertation deals with testing and evaluating off-the-shelf (OTS) sensors and the processing of 3D modelled rooms captured with one of these sensors. The tests performed on the OTS sensors determine the overall accuracy of the sensors for 3D room modelling. The rationale for designing and conducting these tests is to provide the community with suggested practical tests to assist in selecting an OTS sensor for 3D room modelling. The 3D room models are captured using an opensource application and are imported into custom software. The 3D models undergo pre-processing algorithms producing 2D results, which were further processed to determine the walls of rooms. The dimension information about these features are used to create a 2D floor plan. 3D modelled environments are inherently noisy, requiring efficient pre-processing to remove the noise without hampering processing performance of the 3D model. One of the largest contributors to noise and accuracy is the sensor. Selecting the appropriate sensor can mitigate the need for complex pre-processing algorithms and will improve overall processing time. The project was able to extract dimension information within an acceptable error. The tests that were designed and used for sensor testing were able to determine which sensor was the better choice for 3D room modelling. The optimal sensor was found to be Microsoft's Kinect1 . Tests were performed in which the Microsoft Kinect was required to map a room. The results show that dimensional information about the given scene could be successfully extracted with an average error of 4.60 %

Camera Positioning and Vision-based Fall Detection

Elderly care residences often have falling incidents, sometimes with dire consequences. This project aims to implement a method for detecting when people fall, an issue that has seen much research and practical implementations already. In contrast to other work, this project aims to create a non-intrusive, non-wearable solution through the use of cameras. It extends an existing patient monitoring system developed by Eya Solutions, a start-up in the medical field. Their system monitors patients and is composed of embedded systems (clients), a back-end with a database, and a web-based dashboard (the front-end) for administrators, caretakers and users. Our client wants to extend their system by introducing: (i) a video processing component which performs face recognition, (ii) functionality for indoor positioning of people, (iii) functionality to automatically determine near-optimal placement of cameras, and (iv) automatic detection of fall incidents. To understand the relevant research, we survey the field investigating five key research topics: (i) object detection, (ii) fall detection, (iii) facial recognition, (iv) floor plan modelling, and (v) camera placement optimisation. The first part of the project is detecting fall incidents in video footage. By means of image processing (in particular, subsequent frame subtraction), our system detects objects in the foreground and tracks these between frames. Based on the properties of such objects, namely size and shape, we detect fall incidents. The second part of the project is the floor plan editor. This editor is integrated into the existing dashboard, where administrators can model the building. Eya Solutions wishes to provide the product to customers as a complete package, including (i) showing where fall incidents have occurred, and (ii) how and where to place cameras. Our system allows administrators to edit the floor while allowing caretakers to see the modelled floor and see where falls are detected. Considering (ii), it is desirable to generate a configuration where the number of cameras is low while the view coverage is high. Our contribution includes a genetic algorithm which can generate automatically a suitable configuration. Alternatively, cameras can be placed manually by the administrator.Computer Science and Engineerin