Visual Object Tracking: The Initialisation Problem

Model initialisation is an important component of object tracking. Tracking algorithms are generally provided with the first frame of a sequence and a bounding box (BB) indicating the location of the object. This BB may contain a large number of background pixels in addition to the object and can lead to parts-based tracking algorithms initialising their object models in background regions of the BB. In this paper, we tackle this as a missing labels problem, marking pixels sufficiently away from the BB as belonging to the background and learning the labels of the unknown pixels. Three techniques, One-Class SVM (OC-SVM), Sampled-Based Background Model (SBBM) (a novel background model based on pixel samples), and Learning Based Digital Matting (LBDM), are adapted to the problem. These are evaluated with leave-one-video-out cross-validation on the VOT2016 tracking benchmark. Our evaluation shows both OC-SVMs and SBBM are capable of providing a good level of segmentation accuracy but are too parameter-dependent to be used in real-world scenarios. We show that LBDM achieves significantly increased performance with parameters selected by cross validation and we show that it is robust to parameter variation.Comment: 15th Conference on Computer and Robot Vision (CRV 2018). Source code available at https://github.com/georgedeath/initialisation-proble

Object Tracking in Video with Part-Based Tracking by Feature Sampling

Visual tracking of arbitrary objects is an active research topic in computer vision, with applications across multiple disciplines including video surveillance, activity analysis, robot vision, and human computer interface. Despite great progress having been made in object tracking in recent years, it still remains a challenge to design trackers that can deal with difficult tracking scenarios, such as camera motion, object motion change, occlusion, illumination changes, and object deformation. A promising way of tackling these types of problems is to use a part-based method; one which models and tracks small regions of the object and estimates the location of the object based on the tracked part's positions. These approaches typically model parts of objects with histograms of various hand-crafted features extracted from the region in which the part is located. However, it is unclear how such relatively homogeneous regions should be represented to form an effective part-based tracker. In this thesis we present a part-based tracker that includes a model for object parts that is designed to empirically characterise the underlying colour distribution of an image region, representing it by pairs of randomly selected colour features and counts of how many pixels are similar to each feature. This novel feature representation is used to find probable locations for the part in future frames via a Bhattacharyya Distance-based metric, which is modified to prefer higher quality matches. Sets of candidate patch locations are generated by randomly generating non-shearing affine transformations of the part's previous locations and locally optimising the most likely sets of parts to allow for small intra-frame object deformations. We also present a study of model initialisation in online, model-free tracking and evaluate several techniques for selecting the regions of an image, given a target bounding box most likely to contain an object. The strengths and limitations of the combined tracker are evaluated on the VOT2016 and VOT2018 datasets using their evaluation protocol, which also allows an extensive evaluation of parameter robustness. The presented tracker is ranked first among part-based trackers on the VOT2018 dataset and is particularly robust to changes in object and camera motion, as well as object size changes

Context-Aware Generative Models for Prediction of Aircraft Ground Tracks

Trajectory prediction (TP) plays an important role in supporting the decision-making of Air Traffic Controllers (ATCOs). Traditional TP methods are deterministic and physics-based, with parameters that are calibrated using aircraft surveillance data harvested across the world. These models are, therefore, agnostic to the intentions of the pilots and ATCOs, which can have a significant effect on the observed trajectory, particularly in the lateral plane. This work proposes a generative method for lateral TP, using probabilistic machine learning to model the effect of the epistemic uncertainty arising from the unknown effect of pilot behaviour and ATCO intentions. The models are trained to be specific to a particular sector, allowing local procedures such as coordinated entry and exit points to be modelled. A dataset comprising a week's worth of aircraft surveillance data, passing through a busy sector of the United Kingdom's upper airspace, was used to train and test the models. Specifically, a piecewise linear model was used as a functional, low-dimensional representation of the ground tracks, with its control points determined by a generative model conditioned on partial context. It was found that, of the investigated models, a Bayesian Neural Network using the Laplace approximation was able to generate the most plausible trajectories in order to emulate the flow of traffic through the sector

A Probabilistic Model for Aircraft in Climb using Monotonic Functional Gaussian Process Emulators

Ensuring vertical separation is a key means of maintaining safe separation between aircraft in congested airspace. Aircraft trajectories are modelled in the presence of significant epistemic uncertainty, leading to discrepancies between observed trajectories and the predictions of deterministic models, hampering the task of planning to ensure safe separation. In this paper a probabilistic model is presented, for the purpose of emulating the trajectories of aircraft in climb and bounding the uncertainty of the predicted trajectory. A monotonic, functional representation exploits the spatio-temporal correlations in the radar observations. Through the use of Gaussian Process Emulators, features that parameterise the climb are mapped directly to functional outputs, providing a fast approximation, while ensuring that the resulting trajectory is monotonic. The model was applied as a probabilistic digital twin for aircraft in climb and baselined against BADA, a deterministic model widely used in industry. When applied to an unseen test dataset, the probabilistic model was found to provide a mean prediction that was 21% more accurate, with a 34% sharper forecast

Asynchronous ε-greedy Bayesian optimisation

This is the author accepted manuscript. The final version is available from ML Research Press via the link in this recordUAI2021: 37th Conference on Uncertainty in Artificial Intelligence, 27 - 30 July 2021. OnlineBatch Bayesian optimisation (BO) is a successful technique for the optimisation of expensive black-box functions. Asynchronous BO can reduce wallclock time by starting a new evaluation as soon as another finishes, thus maximising resource utilisation. To maximise resource allocation, we develop a novel asynchronous BO method, AEGiS (Asynchronous ε-Greedy Global Search) that combines greedy search, exploiting the surrogate's mean prediction, with Thompson sampling and random selection from the approximate Pareto set describing the trade-off between exploitation (surrogate mean prediction) and exploration (surrogate posterior variance). We demonstrate empirically the efficacy of AEGiS on synthetic benchmark problems, meta-surrogate hyperparameter tuning problems and real-world problems, showing that AEGiS generally outperforms existing methods for asynchronous BO. When a single worker is available performance is no worse than BO using expected improvement.Innovate U

How Bayesian Should Bayesian Optimisation Be?

This is the author accepted manuscript. The final version is available from ACM via the DOI in this recordBayesian optimisation (BO) uses probabilistic surrogate models - usually Gaussian processes (GPs) - for the optimisation of expensive black-box functions. At each BO iteration, the GP hyperparameters are fit to previously-evaluated data by maximising the marginal likelihood. However, this fails to account for uncertainty in the hyperparameters themselves, leading to overconfident model predictions. This uncertainty can be accounted for by taking the Bayesian approach of marginalising out the model hyperparameters. We investigate whether a fully-Bayesian treatment of the Gaussian process hyperparameters in BO (FBBO) leads to improved optimisation performance. Since an analytic approach is intractable, we compare FBBO using three approximate inference schemes to the maximum likelihood approach, using the Expected Improvement (EI) and Upper Confidence Bound (UCB) acquisition functions paired with ARD and isotropic Matern kernels, across 15 well-known benchmark problems for 4 observational noise settings. FBBO using EI with an ARD kernel leads to the best performance in the noise-free setting, with much less difference between combinations of BO components when the noise is increased. FBBO leads to over-exploration with UCB, but is not detrimental with EI. Therefore, we recommend that FBBO using EI with an ARD kernel as the default choice for BO.Innovate U

Greed is Good: Exploration and Exploitation Trade-offs in Bayesian Optimisation

This is the author accepted manuscript. The final version is available from ACM via the DOI in this recordThe performance of acquisition functions for Bayesian optimisation is investigated in terms of the Pareto front between exploration and exploitation. We show that Expected Improvement and the Upper Confidence Bound always select solutions to be expensively evaluated on the Pareto front, but Probability of Improvement is never guaranteed to do so and Weighted Expected Improvement does only for a restricted range of weights. We introduce two novel -greedy acquisition functions. Extensive empirical evaluation of these together with random search, purely exploratory and purely exploitative search on 10 benchmark problems in 1 to 10 dimensions shows that -greedy algorithms are generally at least as effective as conventional acquisition functions, particularly with a limited budget. In higher dimensions -greedy approaches are shown to have improved performance over conventional approaches. These results are borne out on a real world computational fluid dynamics optimisation problem and a robotics active learning problem.Innovate U