Machine Learning Methods for Autonomous Flame Detection in Videos

Fire detection has attracted increasing attention from the public because of the huge loss caused by fires every year. Compared with the traditional fire detection techniques based on smoke or heat sensors, the frameworks using machine learning methods in videos for fire detection have the advantages of higher efficiency and accuracy of detection, robustness to various environments, and lower cost of the systems. The uniqueness of these frameworks stems from the developed machine learning approaches for autonomous information extraction and fire detection in sequential video frames. A framework for flame detection is proposed based on the synergy of the Horn-Schunck optical flow estimation method, a probabilistic saliency analysis approach and a temporal wavelet analysis scheme. The estimated optical flows, together with the saliency analysis method, work effectively in selecting moving regions by well describing the dynamic property of flames, which contributes to accurate detection of flames. Additionally, the temporal wavelet transform based analysis increases the robustness of the framework and provides reliable results by discarding non-flame pixels according to their temporally changing patterns. Apart from the dynamic characteristic of flames, the property of colours is also of crucial importance in describing flames. However, the colours of flames usually vary significantly with different illumination or burning material, which results in a wide diversity. To well model the various colours, a novel flame colour model is proposed based on the Dirichlet process Gaussian mixture model. The distribution of flame colours is represented by a Gaussian mixture model, of which the number of mixture components is learned from the training data autonomously by setting a Dirichlet process as the prior. Compared with those methods which set the number of mixture components empirically, the developed model can access a more accurate estimation of the distribution of flame colours. The inference is successfully implemented by two methods, i.e., the Gibbs sampling and variational inference algorithms, to manage different quantities of training data. The colour model can be incorporated into the framework of flame detection and the results show that the colour model achieves a highly accurate estimation of the distribution of flame colours, which contributes to the good performance of the whole framework. All the proposed approaches are tested on real videos of various environments and proved to be capable of accurate flame detection

Visual Tracking: From An Individual To Groups Of Animals

This thesis is concerned with the development and application of visual tracking techniques to the domain of animal monitoring. The development and evaluation of a system which uses image analysis to control the robotic placement of a sensor on the back of a feeding pig is presented first. This single-target monitoring application is then followed by the evaluation of suitable techniques for tracking groups of animals, of which the most suitable existing technique is found to be a Markov chain Monte Carlo particle filtering algorithm with a Markov random field motion prior (MCMC MRF, Khan et al. 2004). Finally, a new tracking technique is developed which uses social motion information present in groups of social targets to guide the tracking. This is used in the new Motion Parameter Sharing (MPS) algorithm. MPS is designed to improve the tracking of groups of targets with coordinated motion by incorporating motion information from targets that have been moving in a similar way. Situations where coordinated motion information should improve tracking include animal flocking, people moving as a group or any situation where some targets are moving in a correlated fashion. This new method is tested on a variety of real and artificial data sequences, and its performance compared to that of the MCMC MRF algorithm. The new MPS algorithm is found to outperform the MCMC MRF algorithm during a number of different types of sequences (including during occlusion events and noisy sequences) where correlated motion is present between targets. This improvement is apparent both in the accuracy of target location and robustness of tracking, the latter of which is greatly improved

Multimodal, Embodied and Location-Aware Interaction

This work demonstrates the development of mobile, location-aware, eyes-free applications which utilise multiple sensors to provide a continuous, rich and embodied interaction. We bring together ideas from the fields of gesture recognition, continuous multimodal interaction, probability theory and audio interfaces to design and develop location-aware applications and embodied interaction in both a small-scale, egocentric body-based case and a large-scale, exocentric `world-based' case. BodySpace is a gesture-based application, which utilises multiple sensors and pattern recognition enabling the human body to be used as the interface for an application. As an example, we describe the development of a gesture controlled music player, which functions by placing the device at different parts of the body. We describe a new approach to the segmentation and recognition of gestures for this kind of application and show how simulated physical model-based interaction techniques and the use of real world constraints can shape the gestural interaction. GpsTunes is a mobile, multimodal navigation system equipped with inertial control that enables users to actively explore and navigate through an area in an augmented physical space, incorporating and displaying uncertainty resulting from inaccurate sensing and unknown user intention. The system propagates uncertainty appropriately via Monte Carlo sampling and output is displayed both visually and in audio, with audio rendered via granular synthesis. We demonstrate the use of uncertain prediction in the real world and show that appropriate display of the full distribution of potential future user positions with respect to sites-of-interest can improve the quality of interaction over a simplistic interpretation of the sensed data. We show that this system enables eyes-free navigation around set trajectories or paths unfamiliar to the user for varying trajectory width and context. We demon- strate the possibility to create a simulated model of user behaviour, which may be used to gain an insight into the user behaviour observed in our field trials. The extension of this application to provide a general mechanism for highly interactive context aware applications via density exploration is also presented. AirMessages is an example application enabling users to take an embodied approach to scanning a local area to find messages left in their virtual environment

Multimodal, Embodied and Location-Aware Interaction

This work demonstrates the development of mobile, location-aware, eyes-free applications which utilise multiple sensors to provide a continuous, rich and embodied interaction. We bring together ideas from the fields of gesture recognition, continuous multimodal interaction, probability theory and audio interfaces to design and develop location-aware applications and embodied interaction in both a small-scale, egocentric body-based case and a large-scale, exocentric `world-based' case. BodySpace is a gesture-based application, which utilises multiple sensors and pattern recognition enabling the human body to be used as the interface for an application. As an example, we describe the development of a gesture controlled music player, which functions by placing the device at different parts of the body. We describe a new approach to the segmentation and recognition of gestures for this kind of application and show how simulated physical model-based interaction techniques and the use of real world constraints can shape the gestural interaction. GpsTunes is a mobile, multimodal navigation system equipped with inertial control that enables users to actively explore and navigate through an area in an augmented physical space, incorporating and displaying uncertainty resulting from inaccurate sensing and unknown user intention. The system propagates uncertainty appropriately via Monte Carlo sampling and output is displayed both visually and in audio, with audio rendered via granular synthesis. We demonstrate the use of uncertain prediction in the real world and show that appropriate display of the full distribution of potential future user positions with respect to sites-of-interest can improve the quality of interaction over a simplistic interpretation of the sensed data. We show that this system enables eyes-free navigation around set trajectories or paths unfamiliar to the user for varying trajectory width and context. We demon- strate the possibility to create a simulated model of user behaviour, which may be used to gain an insight into the user behaviour observed in our field trials. The extension of this application to provide a general mechanism for highly interactive context aware applications via density exploration is also presented. AirMessages is an example application enabling users to take an embodied approach to scanning a local area to find messages left in their virtual environment

Visual Tracking: From An Individual To Groups Of Animals

This thesis is concerned with the development and application of visual tracking techniques to the domain of animal monitoring. The development and evaluation of a system which uses image analysis to control the robotic placement of a sensor on the back of a feeding pig is presented first. This single-target monitoring application is then followed by the evaluation of suitable techniques for tracking groups of animals, of which the most suitable existing technique is found to be a Markov chain Monte Carlo particle filtering algorithm with a Markov random field motion prior (MCMC MRF, Khan et al. 2004). Finally, a new tracking technique is developed which uses social motion information present in groups of social targets to guide the tracking. This is used in the new Motion Parameter Sharing (MPS) algorithm. MPS is designed to improve the tracking of groups of targets with coordinated motion by incorporating motion information from targets that have been moving in a similar way. Situations where coordinated motion information should improve tracking include animal flocking, people moving as a group or any situation where some targets are moving in a correlated fashion. This new method is tested on a variety of real and artificial data sequences, and its performance compared to that of the MCMC MRF algorithm. The new MPS algorithm is found to outperform the MCMC MRF algorithm during a number of different types of sequences (including during occlusion events and noisy sequences) where correlated motion is present between targets. This improvement is apparent both in the accuracy of target location and robustness of tracking, the latter of which is greatly improved

Cooperative perception for driving applications

An automated vehicle needs to understand its driving environment to operate safely and reliably. This function is performed within the vehicle's perception system, where data from on-board sensors is processed by multiple perception algorithms, including 3D object detection, semantic segmentation and object tracking. To take advantage of different sensor modalities, multiple perception methods fusing the data from on-board cameras and lidars have been devised. However, sensing exclusively from a single vehicle is inherently prone to occlusions and a limited field-of-view that indiscriminately affects all sensor modalities. Alternatively, cooperative perception incorporates sensor observations from multiple view points distributed throughout the driving environment. This research investigates if and how cooperative perception is capable of improving the detection of objects in driving environments using data from multiple, spatially diverse sensors. Over the course of this thesis, four studies are conducted considering different aspects of cooperative perception. The first study considers the various impacts of occlusions and sensor noise on the classification of objects in images and investigates how to fuse data from multiple images. This study serves as a proof-of-concept to validate the core idea of cooperative perception and presents quantitative results on how well cooperative perception can mitigate such impairments. The second study generalises the problem to 3D object detection using infrastructure sensors capable of providing depth information and investigates different sensor fusion approaches for such sensors. Three sensor fusion approaches are devised and evaluated in terms of object detection performance, communication bandwidth and inference time. This study also investigates the impact of the number of sensors in the performance of object detection. The results show that the proposed cooperative 3D object detection method achieves more than thrice the number of correct detections compared to single sensor baselines, while also reducing the number of false positive detections. Next, the problem of optimising the pose of fixed infrastructure sensors in cluttered driving environments is considered. Two novel sensor pose optimisation methods are proposed, one using gradient-based optimisation and one using integer programming techniques, to maximise the visibility of objects. Both use a novel visibility model, based on a rendering engine, capable of determining occlusions between objects. The results suggest that both methods have the potential to guide the cost effective deployment of sensor networks in cooperative perception applications. Finally, the last study considers the problem of estimating the relative pose between non-static sensors relying on sensor data alone. To that end, a novel and computationally efficient point cloud registration method is proposed using a bespoke feature encoder and attention network. Extensive results show that the proposed method is capable of operating in real-time and is more robust for point clouds with low _eld-of-view overlap compared to existing methods

Econometric Advances in Diffusion Models

This thesis gives new and important insights in modeling diffusion data in marketing. It addresses

Reports to the President

A compilation of annual reports for the 1985-1986 academic year, including a report from the President of the Massachusetts Institute of Technology, as well as reports from the academic and administrative units of the Institute. The reports outline the year's goals, accomplishments, honors and awards, and future plans

GVSU Undergraduate and Graduate Catalog, 2008-2009

Grand Valley State University 2008-2009 undergraduate and/or graduate course catalog published annually to provide students with information and guidance for enrollment.https://scholarworks.gvsu.edu/course_catalogs/1083/thumbnail.jp

GVSU Undergraduate and Graduate Catalog, 2014-2015

Grand Valley State University 2014-2015 undergraduate and/or graduate course catalog published annually to provide students with information and guidance for enrollment.https://scholarworks.gvsu.edu/course_catalogs/1089/thumbnail.jp