Video foreground segmentation with deep learning

This thesis tackles the problem of foreground segmentation in videos, even under extremely challenging conditions. This task comes with a plethora of hurdles, as the model needs to distinguish the difference between moving objects and irrelevant background motion which can be caused by the weather, illumination, camera movement etc. As foreground segmentation is often the first step of various highly important applications (video surveillance for security, patient/infant monitoring etc.), it is crucial to develop a model capable of producing excellent results in all kinds of conditions. In order to tackle this problem, we follow the recent trend in other computer vision areas and harness the power of deep learning. We design architectures of convolutional neural networks specifically targeted to counter the aforementioned challenges. We first propose a 3D CNN that models the spatial and temporal information of the scene simultaneously. The network is deep enough to successfully cover more than 50 different scenes of various conditions with no need for any fine-tuning. These conditions include illumination (day or night), weather (sunny, rainy or snowing), background movements (trees moving from the wind, fountains etc) and others. Next, we propose a data augmentation method specifically targeted to illumination changes. We show that artificially augmenting the data set with this method significantly improves the segmentation results, even when tested under sudden illumination changes. We also present a post-processing method that exploits the temporal information of the input video. Finally, we propose a complex deep learning model which learns the illumination of the scene and performs foreground segmentation simultaneously

Higher order and infinite Trotter-number extrapolations in path integral Monte Carlo

Improvements beyond the primitive approximation in the path integral Monte Carlo method are explored both in a model problem and in real systems. Two different strategies are studied: the Richardson extrapolation on top of the path integral Monte Carlo data and the Takahashi-Imada action. The Richardson extrapolation, mainly combined with the primitive action, always reduces the number-of-beads dependence, helps in determining the approach to the dominant power law behavior, and all without additional computational cost. The Takahashi-Imada action has been tested in two hard-core interacting quantum liquids at low temperature. The results obtained show that the fourth-order behavior near the asymptote is conserved, and that the use of this improved action reduces the computing time with respect to the primitive approximation.Comment: 19 pages, RevTex, to appear in J. Chem. Phy

Illumination-Based Data Augmentation for Robust Background Subtraction

A core challenge in background subtraction (BGS) is handling videos with sudden illumination changes in consecutive frames. In this paper, we tackle the problem from a data point-of-view using data augmentation. Our method performs data augmentation that not only creates endless data on the fly, but also features semantic transformations of illumination which enhance the generalisation of the model. It successfully simulates flashes and shadows by applying the Euclidean distance transform over a binary mask generated randomly. Such data allows us to effectively train an illumination-invariant deep learning model for BGS. Experimental results demonstrate the contribution of the synthetics in the ability of the models to perform BGS even when significant illumination changes take place

Existentialism and feminism in Kezilahabi`s novel Kichwamaji

Makala hii inachambua riwaya ya pili ya mwandishi maarufu wa Kiswahili, Euphrase Kezilahabi (*1944) iitwayo Kichwamaji (1974). Inajaribu kuzingatia mikondo miwili ya uchambuzi yaani inajadili kwa ufupi nadharia ipi au mkondo upi wa kimawazo unafaa zaidi katika kuichambua riwaya hiyo: udhanaishi au ufeministi. Je, inawezekana kuunganisha yote mawili?In this essay, I would like to analyse the novel Kichwamaji (‘Empty-head’; 1974) by the well-known Tanzanian writer Euphrase Kezilahabi against the background of two philosophical theories: existentialism and feminism. I will first discuss existentialism and the existentialist elements in the novel. Then I will present feminist theory and focus on the female characters in Kichwamaji. I will argue that a feminist reading of the novel is impossible due to its predominant existentialist character

High order Chin actions in path integral Monte Carlo

High order actions proposed by Chin have been used for the first time in path integral Monte Carlo simulations. Contrarily to the Takahashi-Imada action, which is accurate to fourth order only for the trace, the Chin action is fully fourth order, with the additional advantage that the leading fourth and sixth order error coefficients are finely tunable. By optimizing two free parameters entering in the new action we show that the time step error dependence achieved is best fitted with a sixth order law. The computational effort per bead is increased but the total number of beads is greatly reduced, and the efficiency improvement with respect to the primitive approximation is approximately a factor of ten. The Chin action is tested in a one-dimensional harmonic oscillator, a H$_2$ drop, and bulk liquid $^4$He. In all cases a sixth-order law is obtained with values of the number of beads that compare well with the pair action approximation in the stringent test of superfluid $^4$He.Comment: 19 pages, 8 figure

Unifying Person and Vehicle Re-Identification

Person and vehicle re-identification (re-ID) are important challenges for the analysis of the burgeoning collection of urban surveillance videos. To efficiently evaluate such videos, which are populated with both vehicles and pedestrians, it would be preferable to have one unified framework with effective performance across both domains. Unfortunately, due to the contrasting composition of humans and vehicles, no architecture has yet been established that can adequately perform both tasks. We release a Person and Vehicle Unified Data Set (PVUD) comprising of both pedestrians and vehicles from popular existing re-ID data sets, in order to better model the data that we would expect to find in the real world. We exploit the generalisation ability of metric learning to propose a re-ID framework that can learn to re-identify humans and vehicles simultaneously. We design our network, MidTriNet, to harness the power of mid-level features to develop better representations for the re-ID tasks. We help the system to handle mixed data by appending unification terms with additional hard negative and hard positive mining to MidTriNet. We attain comparable accuracy training on PVUD to training on the comprising data sets separately, supporting the system's generalisation power. To further demonstrate the effectiveness of our framework, we also obtain results better than, or competitive with, the state-of-the-art on each of the Market-1501, CUHK03, VehicleID and VeRi data sets