Gaussian mixture model classifiers for detection and tracking in UAV video streams.

Masters Degree. University of KwaZulu-Natal, Durban.Manual visual surveillance systems are subject to a high degree of human-error and operator fatigue. The automation of such systems often employs detectors, trackers and classifiers as fundamental building blocks. Detection, tracking and classification are especially useful and challenging in Unmanned Aerial Vehicle (UAV) based surveillance systems. Previous solutions have addressed challenges via complex classification methods. This dissertation proposes less complex Gaussian Mixture Model (GMM) based classifiers that can simplify the process; where data is represented as a reduced set of model parameters, and classification is performed in the low dimensionality parameter-space. The specification and adoption of GMM based classifiers on the UAV visual tracking feature space formed the principal contribution of the work. This methodology can be generalised to other feature spaces. This dissertation presents two main contributions in the form of submissions to ISI accredited journals. In the first paper, objectives are demonstrated with a vehicle detector incorporating a two stage GMM classifier, applied to a single feature space, namely Histogram of Oriented Gradients (HoG). While the second paper demonstrates objectives with a vehicle tracker using colour histograms (in RGB and HSV), with Gaussian Mixture Model (GMM) classifiers and a Kalman filter. The proposed works are comparable to related works with testing performed on benchmark datasets. In the tracking domain for such platforms, tracking alone is insufficient. Adaptive detection and classification can assist in search space reduction, building of knowledge priors and improved target representations. Results show that the proposed approach improves performance and robustness. Findings also indicate potential further enhancements such as a multi-mode tracker with global and local tracking based on a combination of both papers

The University Defence Research Collaboration In Signal Processing

This chapter describes the development of algorithms for automatic detection of anomalies from multi-dimensional, undersampled and incomplete datasets. The challenge in this work is to identify and classify behaviours as normal or abnormal, safe or threatening, from an irregular and often heterogeneous sensor network. Many defence and civilian applications can be modelled as complex networks of interconnected nodes with unknown or uncertain spatio-temporal relations. The behavior of such heterogeneous networks can exhibit dynamic properties, reflecting evolution in both network structure (new nodes appearing and existing nodes disappearing), as well as inter-node relations. The UDRC work has addressed not only the detection of anomalies, but also the identification of their nature and their statistical characteristics. Normal patterns and changes in behavior have been incorporated to provide an acceptable balance between true positive rate, false positive rate, performance and computational cost. Data quality measures have been used to ensure the models of normality are not corrupted by unreliable and ambiguous data. The context for the activity of each node in complex networks offers an even more efficient anomaly detection mechanism. This has allowed the development of efficient approaches which not only detect anomalies but which also go on to classify their behaviour

The University Defence Research Collaboration In Signal Processing: 2013-2018

Signal processing is an enabling technology crucial to all areas of defence and security. It is called for whenever humans and autonomous systems are required to interpret data (i.e. the signal) output from sensors. This leads to the production of the intelligence on which military outcomes depend. Signal processing should be timely, accurate and suited to the decisions to be made. When performed well it is critical, battle-winning and probably the most important weapon which you’ve never heard of. With the plethora of sensors and data sources that are emerging in the future network-enabled battlespace, sensing is becoming ubiquitous. This makes signal processing more complicated but also brings great opportunities. The second phase of the University Defence Research Collaboration in Signal Processing was set up to meet these complex problems head-on while taking advantage of the opportunities. Its unique structure combines two multi-disciplinary academic consortia, in which many researchers can approach different aspects of a problem, with baked-in industrial collaboration enabling early commercial exploitation. This phase of the UDRC will have been running for 5 years by the time it completes in March 2018, with remarkable results. This book aims to present those accomplishments and advances in a style accessible to stakeholders, collaborators and exploiters

VGC 2023 - Unveiling the dynamic Earth with digital methods: 5th Virtual Geoscience Conference: Book of Abstracts

Conference proceedings of the 5th Virtual Geoscience Conference, 21-22 September 2023, held in Dresden. The VGC is a multidisciplinary forum for researchers in geoscience, geomatics and related disciplines to share their latest developments and applications.:Short Courses 9 Workshops Stream 1 10 Workshop Stream 2 11 Workshop Stream 3 12 Session 1 – Point Cloud Processing: Workflows, Geometry & Semantics 14 Session 2 – Visualisation, communication & Teaching 27 Session 3 – Applying Machine Learning in Geosciences 36 Session 4 – Digital Outcrop Characterisation & Analysis 49 Session 5 – Airborne & Remote Mapping 58 Session 6 – Recent Developments in Geomorphic Process and Hazard Monitoring 69 Session 7 – Applications in Hydrology & Ecology 82 Poster Contributions 9

The visual object tracking VOT2015 challenge results

The Visual Object Tracking challenge 2015, VOT2015, aims at comparing short-term single-object visual trackers that do not apply pre-learned models of object appearance. Results of 62 trackers are presented. The number of tested trackers makes VOT 2015 the largest benchmark on short-term tracking to date. For each participating tracker, a short description is provided in the appendix. Features of the VOT2015 challenge that go beyond its VOT2014 predecessor are: (i) a new VOT2015 dataset twice as large as in VOT2014 with full annotation of targets by rotated bounding boxes and per-frame attribute, (ii) extensions of the VOT2014 evaluation methodology by introduction of a new performance measure. The dataset, the evaluation kit as well as the results are publicly available at the challenge website

Modeling and simulation of adaptive multimodal optical sensors for target tracking in the visible to near infrared

This work investigates an integrated aerial remote sensor design approach to address moving target detection and tracking problems within highly cluttered, dynamic ground-based scenes. Sophisticated simulation methodologies and scene phenomenology validations have resulted in advancements in artificial multimodal truth video synthesis. Complex modeling of novel micro-opto-electro-mechanical systems (MOEMS) devices, optical systems, and detector arrays has resulted in a proof of concept for a state-of-the-art imaging spectropolarimeter sensor model that does not suffer from typical multimodal image registration problems. Test methodology developed for this work provides the ability to quantify performance of a target tracking application with varying ground scenery, flight characteristics, or sensor specifications. The culmination of this research is an end-to-end simulated demonstration of multimodal aerial remote sensing and target tracking. Deeply hidden target recognition is shown to be enhanced through the fusing of panchromatic, hyperspectral, and polarimetric image modalities. The Digital Imaging and Remote Sensing Image Generation model was leveraged to synthesize truth spectropolarimetric sensor-reaching radiance image cubes comprised of coregistered Stokes vector bands in the visible to near-infrared. An intricate synthetic urban scene containing numerous moving vehicular targets was imaged from a virtual sensor aboard an aerial platform encircling a stare point. An adaptive sensor model was designed with a superpixel array of MOEMS devices fabricated atop a division of focal plane detector. Degree of linear polarization (DoLP) imagery is acquired by combining three adjacent micropolarizer outputs within each 2x2 superpixel whose respective transmissions vary with wavelength, relative angle of polarization, and wire-grid spacing. A novel micromirror within each superpixel adaptively relays light between a panchromatic imaging channel and a hyperspectral spectrometer channel. All optical and detector sensor effects were radiometrically modeled using MATLAB and optical lens design software. Orthorectification of all sensor outputs yields multimodal pseudonadir observation video at a fixed ground sampled distance across an area of responsibility. A proprietary MATLAB-based target tracker accomplishes change detection between sequential panchromatic or DoLP observation frames, and queries the sensor for hyperspectral pixels to aid in track initialization and maintenance. Image quality, spectral quality, and tracking performance metrics are reported for varying scenario parameters including target occlusions within the scene, declination angle and jitter of the aerial platform, micropolarizer diattenuation, and spectral/spatial resolution of the adaptive sensor outputs. DoLP observations were found to track moving vehicles better than panchromatic observations at high oblique angles when facing the sensor generally toward the sun. Vehicular occlusions due to tree canopies and parallax effects of tall buildings significantly reduced tracking performance as expected. Smaller MOEMS pixel sizes drastically improved track performance, but also generated a significant number of false tracks. Atmospheric haze from urban aerosols eliminated the tracking utility of DoLP observations, while aerial platform jitter without image stabilization eliminated tracking utility in both modalities. Wire-grid micropolarizers with very low VNIR diattenuation were found to still extinguish enough cross-polarized light to successfully distinguish and track moving vehicles from their urban background. Thus, state-of-the-art lithographic techniques to create finer wire-grid spacings that exhibit high VNIR diattenuation may not be required

Remote Sensing

This dual conception of remote sensing brought us to the idea of preparing two different books; in addition to the first book which displays recent advances in remote sensing applications, this book is devoted to new techniques for data processing, sensors and platforms. We do not intend this book to cover all aspects of remote sensing techniques and platforms, since it would be an impossible task for a single volume. Instead, we have collected a number of high-quality, original and representative contributions in those areas

Very High Resolution (VHR) Satellite Imagery: Processing and Applications

Recently, growing interest in the use of remote sensing imagery has appeared to provide synoptic maps of water quality parameters in coastal and inner water ecosystems;, monitoring of complex land ecosystems for biodiversity conservation; precision agriculture for the management of soils, crops, and pests; urban planning; disaster monitoring, etc. However, for these maps to achieve their full potential, it is important to engage in periodic monitoring and analysis of multi-temporal changes. In this context, very high resolution (VHR) satellite-based optical, infrared, and radar imaging instruments provide reliable information to implement spatially-based conservation actions. Moreover, they enable observations of parameters of our environment at greater broader spatial and finer temporal scales than those allowed through field observation alone. In this sense, recent very high resolution satellite technologies and image processing algorithms present the opportunity to develop quantitative techniques that have the potential to improve upon traditional techniques in terms of cost, mapping fidelity, and objectivity. Typical applications include multi-temporal classification, recognition and tracking of specific patterns, multisensor data fusion, analysis of land/marine ecosystem processes and environment monitoring, etc. This book aims to collect new developments, methodologies, and applications of very high resolution satellite data for remote sensing. The works selected provide to the research community the most recent advances on all aspects of VHR satellite remote sensing