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

Methane Mitigation:Methods to Reduce Emissions, on the Path to the Paris Agreement

The atmospheric methane burden is increasing rapidly, contrary to pathways compatible with the goals of the 2015 United Nations Framework Convention on Climate Change Paris Agreement. Urgent action is required to bring methane back to a pathway more in line with the Paris goals. Emission reduction from “tractable” (easier to mitigate) anthropogenic sources such as the fossil fuel industries and landfills is being much facilitated by technical advances in the past decade, which have radically improved our ability to locate, identify, quantify, and reduce emissions. Measures to reduce emissions from “intractable” (harder to mitigate) anthropogenic sources such as agriculture and biomass burning have received less attention and are also becoming more feasible, including removal from elevated-methane ambient air near to sources. The wider effort to use microbiological and dietary intervention to reduce emissions from cattle (and humans) is not addressed in detail in this essentially geophysical review. Though they cannot replace the need to reach “net-zero” emissions of CO2, significant reductions in the methane burden will ease the timescales needed to reach required CO2 reduction targets for any particular future temperature limit. There is no single magic bullet, but implementation of a wide array of mitigation and emission reduction strategies could substantially cut the global methane burden, at a cost that is relatively low compared to the parallel and necessary measures to reduce CO2, and thereby reduce the atmospheric methane burden back toward pathways consistent with the goals of the Paris Agreement

Traffic monitoring using video analytics in clouds

Traffic monitoring is a challenging task on crowded roads. Traditional traffic monitoring procedures are manual, expensive, time consuming and involve human operators. They are subjective due to the very involvement of human factor and sometimes provide inaccurate/incomplete monitoring results. Large scale storage and analysis of video streams were not possible due to limited availability of storage and compute resources in the past. Recent advances in data storage, processing and communications have made it possible to store and process huge volumes of video data and develop applications that are neither subjective nor limited in feature sets. It is now possible to implement object detection and tracking, behavioural analysis of traffic patterns, number plate recognition and automate security and surveillance on video streams produced by traffic monitoring and surveillance cameras. In this paper, we present a video stream acquisition, processing and analytics framework in the clouds to address some of the traffic monitoring challenges mentioned above. This framework provides an end-to-end solution for video stream capture, storage and analysis using a cloud based GPU cluster. The framework empowers traffic control room operators by automating the process of vehicle identification and finding events of interest from the recorded video streams. An operator only specifies the analysis criteria and the duration of video streams to analyse. The video streams are then automatically fetched from the cloud storage, decoded and analysed on a Hadoop based GPU cluster without operator intervention in our framework. It reduces the latencies in video analysis process by porting its compute intensive parts to the GPU cluster. The framework is evaluated with one month of recorded video streams data on a cloud based GPU cluster. The results show a speedup of 14 times on a GPU and 4 times on a CPU when compared with one human operator analysing the same amount of video streams data

Stress and cognition : mechanisms regulating memory and empathy

Stress kan cognitie op vele manieren beïnvloeden. Het is algemeen bekend dat niet al onze herinneringen even sterk zijn. Emotionele en traumatische levenservaringen worden beter opgeslagen in ons geheugen dan alledaagse gebeurtenissen. Daartegenover is het bekend dat stress juist het oproepen van eerder aangeleerde informatie uit ons geheugen kan verminderen. Voorbeelden van dit verschijnsel zijn de moeilijkheden die we kunnen hebben met het herinneren van simpele informatie tijdens een stressvol examen of sollicitatiegesprek. Eerder onderzoek heeft aangetoond dat stresshormonen (glucocorticoïden en adrenaline) die vrijkomen uit de bijnieren tijdens stressvolle gebeurtenissen een belangrijke rol spelen bij beide processen. Van oudsher is bekend dat glucocorticoïdhormonen werken via een langzaam mechanisme door middel van het beïnvloeden van genexpressie. Recent onderzoek heeft echter bewezen dat er ook hele snelle, niet-genomische glucocorticoïd effecten zijn. Maar het mechanisme dat hier achter zit is nauwelijks bekend. De resultaten van dit proefschift tonen aan dat het endocannabinoïde systeem, een snelwerkend lipide systeem in de hersenen, vooral bekend vanwege de psychoactieve effecten van cannabis, onmisbaar is bij deze snelle geheugeneffecten van glucocorticoïden. Deze bevindingen kunnen niet alleen leiden tot nieuwe inzichten bij het bestrijden van emotionele of traumatische geheugenprocessen in mensen met een post-traumatische stressstoornis maar kunnen mogelijk ook verklaren waarom het gebruik van cannabis zo hoog is in mensen met traumatische levenservaringen. Stress has multifaceted effects on cognition. It is well known that memories are not created equally, for instance emotionally arousing and traumatic life events are consolidated and thereby remembered better. This is an evolutionarily important mechanism since it promotes the remembrance of events that are significant for the survival of the organism. On the other hand, stress acutely impairs the retrieval of previously learned information. Daily life examples of this phenomenon could be the difficulty in retrieving information during an important exam or job interview. Prior studies indicated that both effects result from the influences of hormones (glucocorticoids and epinephrine) released from adrenal glands during stressful episodes. Classically, glucocorticoids act through slow genomic pathways that involve transcriptional regulation that take hours to emerge but they can also exert nongenomic actions (actions not involving transcriptional events) in a rapid fashion. The neurobiological mechanisms that are responsible for mediating these rapid glucocorticoid actions are however poorly understood. The findings of this thesis indicate that the endocannabinoid system, a fast-acting lipid system in the brain, mainly known for mediating the psychoactive effects of cannabis, is crucially involved in regulating the rapid effects of glucocorticoids on both the consolidation and retrieval of emotionally arousing experiences. These findings might not only result in the development of new treatment strategies for people with traumatic memories and posttraumatic stress disorder, but they might also explain why the prevalence of cannabis use is so high in people suffering from traumatic experiences.