Multiple-objective sensor management and optimisation

One of the key challenges associated with exploiting modern Autonomous Vehicle technology for military surveillance tasks is the development of Sensor Management strategies which maximise the performance of the on-board Data-Fusion systems. The focus of this thesis is the development of Sensor Management algorithms which aim to optimise target tracking processes. Three principal theoretical and analytical contributions are presented which are related to the manner in which such problems are formulated and subsequently solved.Firstly, the trade-offs between optimising target tracking and other system-level objectives relating to expected operating lifetime are explored in an autonomous ground sensor scenario. This is achieved by modelling the observer trajectory control design as a probabilistic, information-theoretic, multiple-objective optimisation problem. This novel approach explores the relationships between the changes in sensor-target geometry that are induced by tracking performance measures and those relating to power consumption. This culminates in a novel observer trajectory control algorithm based onthe minimax approach.The second contribution is an analysis of the propagation of error through a limited-lookahead sensor control feedback loop. In the last decade, it has been shown that the use of such non-myopic (multiple-step) planning strategies can lead to superior performance in many Sensor Management scenarios. However, relatively little is known about the performance of strategies which use different horizon lengths. It is shown that, in the general case, planning performance is a function of the length of the horizon over which the optimisation is performed. While increasing the horizon maximises the chances of achieving global optimality, by revealing information about the substructureof the decision space, it also increases the impact of any prediction error, approximations, or unforeseen risk present within the scenario. These competing mechanisms aredemonstrated using an example tracking problem. This provides the motivation for a novel sensor control methodology that employs an adaptive length optimisation horizon. A route to selecting the optimal horizon size is proposed, based on a new non-myopic risk equilibrium which identifies the point where the two competing mechanisms are balanced.The third area of contribution concerns the development of a number of novel optimisation algorithms aimed at solving the resulting sequential decision making problems. These problems are typically solved using stochastic search methods such as Genetic Algorithms or Simulated Annealing. The techniques presented in this thesis are extensions of the recently proposed Repeated Weighted Boosting Search algorithm. In its originalform, it is only applicable to continuous, single-objective, ptimisation problems. The extensions facilitate application to mixed search spaces and Pareto multiple-objective problems. The resulting algorithms have performance comparable with Genetic Algorithm variants, and offer a number of advantages such as ease of implementation and limited tuning requirements

Generative Adversarial Networks for Online Visual Object Tracking Systems

Object Tracking is one of the essential tasks in computer vision domain as it has numerous applications in various fields, such as human-computer interaction, video surveillance, augmented reality, and robotics. Object Tracking refers to the process of detecting and locating the target object in a series of frames in a video. The state-of-the-art for tracking-by-detection framework is typically made up of two steps to track the target object. The first step is drawing multiple samples near the target region of the previous frame. The second step is classifying each sample as either the target object or the background. Visual object tracking remains one of the most challenging task due to variations in visual data such as target occlusion, background clutter, illumination changes, scale changes, as well as challenges stem from the tracking problem including fast motion, out of view, motion blur, deformation, and in and out planar rotation. These challenges continue to be tackled by researchers as they investigate more effective algorithms that are able to track any object under various changing conditions. To keep the research community motivated, there are several annual tracker benchmarking competitions organized to consolidate performance measures and evaluation protocols in different tracking subfields such as Visual Object Tracking VOT challenges and The Multiple Object Tracking MOT Challenges [1, 2]. Despite the excellent performance achieved with deep learning, modern deep tracking methods are still limited in several aspects. The variety of appearance changes over time remains a problem for deep trackers, owing to spatial overlap between positive samples. Furthermore, existing methods require high computational load and suffer from slow running speed. Recently, Generative Adversarial Networks (GANs) have shown excellent results in solving a variety of computer vision problems, making them attractive in investigating their potential use in achieving better results in other computer vision applications, namely, visual object tracking. In this thesis, we explore the impact of using Residual Network ResNet as an alternative feature extractor to Visual Geometry Group VGG which is commonly used in literature. Furthermore, we attempt to address the limitations of object tracking by exploiting the ongoing advancement in Generative Adversarial Networks. We describe a generative adversarial network intended to improve the tracker’s classifier during the online training phase. The network generates adaptive masks to augment the positive samples detected by the convolutional layer of the tracker’s model in order to improve the model’s classifier by making the samples more difficult. Then we integrate this network with Multi-Domain Convolutional Neural Network (MDNet) tracker and present the results. Furthermore, we introduce a novel tracker, MDResNet, by substituting the convolutional layers of MDNet that were originally taken from Visual Geometry Group (VGG-M) network with layers taken from Residual Deep Network (ResNet-50) and the results are compared. We also introduce a new tracker, Region of Interest with Adversarial Learning (ROIAL), by integrating the generative adversarial network with the Real-Time Multi-Domain Convolutional Network (RT-MDNet) tracker. We also integrate the GAN network with MDResNet and MDNet and compare the results with ROIAL

Visual object tracking performance measures revisited

The problem of visual tracking evaluation is sporting a large variety of performance measures, and largely suffers from lack of consensus about which measures should be used in experiments. This makes the cross-paper tracker comparison difficult. Furthermore, as some measures may be less effective than others, the tracking results may be skewed or biased towards particular tracking aspects. In this paper we revisit the popular performance measures and tracker performance visualizations and analyze them theoretically and experimentally. We show that several measures are equivalent from the point of information they provide for tracker comparison and, crucially, that some are more brittle than the others. Based on our analysis we narrow down the set of potential measures to only two complementary ones, describing accuracy and robustness, thus pushing towards homogenization of the tracker evaluation methodology. These two measures can be intuitively interpreted and visualized and have been employed by the recent Visual Object Tracking (VOT) challenges as the foundation for the evaluation methodology

Towards a Principled Integration of Multi-Camera Re-Identification and Tracking through Optimal Bayes Filters

With the rise of end-to-end learning through deep learning, person detectors and re-identification (ReID) models have recently become very strong. Multi-camera multi-target (MCMT) tracking has not fully gone through this transformation yet. We intend to take another step in this direction by presenting a theoretically principled way of integrating ReID with tracking formulated as an optimal Bayes filter. This conveniently side-steps the need for data-association and opens up a direct path from full images to the core of the tracker. While the results are still sub-par, we believe that this new, tight integration opens many interesting research opportunities and leads the way towards full end-to-end tracking from raw pixels.Comment: First two authors have equal contribution. This is initial work into a new direction, not a benchmark-beating method. v2 only adds acknowledgements and fixes a typo in e-mai