Bayesian Approaches to Tracking, Sensor Fusion and Intent Prediction

This thesis presents work on the development of model-based Bayesian approaches to object tracking and intent prediction. Successful navigation/positioning applications rely fundamentally on the choice of appropriate dynamic model and the design of effective tracking algorithms capable of maximising the use of the structure of the dynamic system and the information from sensors. While the tracking problem with frequent and accurate position data has been well studied, we push back the frontiers of current technology where an object can undergo fast manoeuvres and position fixes are limited. On the other hand, intent prediction techniques which extract higher level information such as the intended destination of a moving object can be designed, given the ability to perform successful tracking. Such techniques can play important roles in various application areas, including traffic monitoring, intelligent human computer interaction systems and autonomous route planning. In the first part of this thesis Bayesian tracking methods are designed based on a standard fix-rate setting in which the dynamic system is formulated into a Markovian state space form. We show that the combination of an intrinsic coordinate dynamic model and sensors in the object's body frame leads to novel state space models according to which efficient proposal kernels can be designed and implemented by the sequential Monte Carlo (SMC) methods. Also, sequential Markov chain Monte Carlo schemes are considered for the first time to tackle the sequential batch inference problems due to the presence of infrequent position data. Performance evaluation on both synthetic and real-world data shows that the proposed algorithms are superior to simpler particle filters, implying that they can be favourable alternatives to tracking problems with inertial sensors. The modelling assumption that leads to Markovian state space models can be restrictive for real-world systems as it stipulates that the state sequence has to be synchronised with the observations. In the second major part of this thesis we relax this assumption and work with a more natural class of models, termed variable rate models. We generalise the existing variable rate intrinsic model to incorporate acceleration, speed, distance and position data and introduce new variable rate particle filtering methods tailored to the derived model to accommodate multi-sensor multi-rate tracking scenarios. The proposed algorithms can achieve substantial improvements in terms of tracking accuracy and robustness over a bootstrap variable rate particle filter. Moreover, full Bayesian inference schemes for the learning of both the hidden state and system parameters are presented, with numerical results illustrating their effectiveness. The last part of the thesis is about designing efficient intent prediction algorithms within a Bayesian framework. A pseudo-observation based approach to the incorporation of destination knowledge is introduced, making the mathematics of the dynamical model and the observation process consistent with the Markov state process. Based on the new interpretation, two algorithms are proposed to sequentially estimate the probability of all possible endpoints. Whilst the synthetic maritime surveillance data demonstrate that the proposed methods can achieve comparable prediction performance with reduced computational cost in comparison to the existing bridging distribution based methods, the results on an extensive freehand pointing database, which contains 95 three-dimensional pointing trajectories, show that the new algorithms can outperform other state-of-the-art techniques. Some sensitivity tests are also performed, confirming the good robustness of the introduced methods against model mismatches

GreatSplicing: A Semantically Rich Splicing Dataset

In existing splicing forgery datasets, the insufficient semantic varieties of spliced regions cause a problem that trained detection models overfit semantic features rather than splicing traces. Meanwhile, because of the absence of a reasonable dataset, different detection methods proposed cannot reach a consensus on experimental settings. To address these urgent issues, GreatSplicing, a manually created splicing dataset with a considerable amount and high quality, is proposed in this paper. GreatSplicing comprises 5,000 spliced images and covers spliced regions with 335 distinct semantic categories, allowing neural networks to grasp splicing traces better. Extensive experiments demonstrate that models trained on GreatSplicing exhibit minimal misidentification rates and superior cross-dataset detection capabilities compared to existing datasets. Furthermore, GreatSplicing is available for all research purposes and can be downloaded from www.greatsplicing.net

A Proximal Algorithm for Sampling

We study sampling problems associated with potentials that lack smoothness. The potentials can be either convex or non-convex. Departing from the standard smooth setting, the potentials are only assumed to be weakly smooth or non-smooth, or the summation of multiple such functions. We develop a sampling algorithm that resembles proximal algorithms in optimization for this challenging sampling task. Our algorithm is based on a special case of Gibbs sampling known as the alternating sampling framework (ASF). The key contribution of this work is a practical realization of the ASF based on rejection sampling for both non-convex and convex potentials that are not necessarily smooth. In almost all the cases of sampling considered in this work, our proximal sampling algorithm achieves better complexity than all existing methods.Comment: 26 page

A unified analysis of a class of proximal bundle methods for solving hybrid convex composite optimization problems

This paper presents a proximal bundle (PB) framework based on a generic bundle update scheme for solving the hybrid convex composite optimization (HCCO) problem and establishes a common iteration-complexity bound for any variant belonging to it. As a consequence, iteration-complexity bounds for three PB variants based on different bundle update schemes are obtained in the HCCO context for the first time and in a unified manner. While two of the PB variants are universal (i.e., their implementations do not require parameters associated with the HCCO instance), the other newly (as far as the authors are aware of) proposed one is not but has the advantage that it generates simple, namely one-cut, bundle models. The paper also presents a universal adaptive PB variant (which is not necessarily an instance of the framework) based on one-cut models and shows that its iteration-complexity is the same as the two aforementioned universal PB variants.Comment: 31 page

Memorable Worlds - City Representation and Planning in Video Games

This bachelor’s thesis examines the representation of cities in open world video games. It explores the many techniques and practices for creating immersive game city environments, and the unique planning criteria that they employ. The subjects for analysis are open world video games and their inhabited cities. Firstly, the vital importance of immersion to designing game worlds is defined and highlighted, and the scarcity of academic work on the topic is established. Secondly, literature regarding game cities and immersion are discussed and presented as part of the framework for the following analysis phase. The best practices establish three significant game cities from the 2010s to present; under observation are their means of achieving a memorable and believable city image and techniques to produce immersion. Finally, the Findings-chapter relays the observations from the best practices and analyzes them in regards to the literature. This study discovers that game cities heavily emphasize visual imagery and rely on strong narratives in creating tone and immersion. Elements such as social dimension and city image take up new meaning and weight in game cities. It is reasoned that games have their own criteria and techniques to aid immersion. Furthermore, this thesis also facilitates the separation of virtual planning from traditional planning criteria - immersion is presented to occur when game city elements occupy the intended tone and narrative

Jump Particle Filtering Framework for Joint Target Tracking and Intent Recognition

This paper presents a Bayesian framework for inferring the posterior of the extended state of a target, incorporating its underlying goal or intent, such as any intermediate waypoints and/or final destination. The methodology is thus for joint tracking and intent recognition. Several novel latent intent models are proposed here within a virtual leader formulation. They capture the influence of the target's hidden goal on its instantaneous behaviour. In this context, various motion models, including for highly maneuvering objects, are also considered. The a priori unknown target intent (e.g. destination) can dynamically change over time and take any value within the state space (e.g. a location or spatial region). A sequential Monte Carlo (particle filtering) approach is introduced for the simultaneous estimation of the target's (kinematic) state and its intent. Rao-Blackwellisation is employed to enhance the statistical performance of the inference routine. Simulated data and real radar measurements are used to demonstrate the efficacy of the proposed techniques.Comment: Submitted to IEEE Transactions on Aerospace and Electronic Systems (T-AES

MPI-Flow: Learning Realistic Optical Flow with Multiplane Images

The accuracy of learning-based optical flow estimation models heavily relies on the realism of the training datasets. Current approaches for generating such datasets either employ synthetic data or generate images with limited realism. However, the domain gap of these data with real-world scenes constrains the generalization of the trained model to real-world applications. To address this issue, we investigate generating realistic optical flow datasets from real-world images. Firstly, to generate highly realistic new images, we construct a layered depth representation, known as multiplane images (MPI), from single-view images. This allows us to generate novel view images that are highly realistic. To generate optical flow maps that correspond accurately to the new image, we calculate the optical flows of each plane using the camera matrix and plane depths. We then project these layered optical flows into the output optical flow map with volume rendering. Secondly, to ensure the realism of motion, we present an independent object motion module that can separate the camera and dynamic object motion in MPI. This module addresses the deficiency in MPI-based single-view methods, where optical flow is generated only by camera motion and does not account for any object movement. We additionally devise a depth-aware inpainting module to merge new images with dynamic objects and address unnatural motion occlusions. We show the superior performance of our method through extensive experiments on real-world datasets. Moreover, our approach achieves state-of-the-art performance in both unsupervised and supervised training of learning-based models. The code will be made publicly available at: \url{https://github.com/Sharpiless/MPI-Flow}.Comment: Accepted to ICCV202

A single cut proximal bundle method for stochastic convex composite optimization

In this paper, we consider optimization problems where the objective is the sum of a function given by an expectation and a Lipschitz continuous convex function. For such problems, we propose a Stochastic Composite Proximal Bundle (SCPB) method with optimal complexity. The method does not require estimation of parameters involved in the assumptions on the objective functions. Moreover, to the best of our knowledge, this is the first proximal bundle method for stochastic programming able to deal with continuous distributions. Finally, we present the results of numerical experiments where SCPB slightly outperforms Stochastic Mirror Descent.Comment: 23 page