A Universal Update-pacing Framework For Visual Tracking

This paper proposes a novel framework to alleviate the model drift problem in visual tracking, which is based on paced updates and trajectory selection. Given a base tracker, an ensemble of trackers is generated, in which each tracker's update behavior will be paced and then traces the target object forward and backward to generate a pair of trajectories in an interval. Then, we implicitly perform self-examination based on trajectory pair of each tracker and select the most robust tracker. The proposed framework can effectively leverage temporal context of sequential frames and avoid to learn corrupted information. Extensive experiments on the standard benchmark suggest that the proposed framework achieves superior performance against state-of-the-art trackers.Comment: Submitted to ICIP 201

Sim-to-Real Reinforcement Learning Framework for Autonomous Aerial Leaf Sampling

Using unmanned aerial systems (UAS) for leaf sampling is contributing to a better understanding of the influence of climate change on plant species, and the dynamics of forest ecology by studying hard-to-reach tree canopies. Currently, multiple skilled operators are required for UAS maneuvering and using the leaf sampling tool. This often limits sampling to only the canopy top or periphery. Sim-to-real reinforcement learning (RL) can be leveraged to tackle challenges in the autonomous operation of aerial leaf sampling in the changing environment of a tree canopy. However, trans- ferring an RL controller that is learned in simulation to real UAS applications is challenging due to the risk of crashes. UAS crashes pose safety risks to the operator and its surroundings which often leads to expensive UAS repairs. In this thesis, we present a Sim-to-Real Transfer framework using a computer numerical control (CNC) platform as a safer, and more robust proxy, before using the controller on a UAS. In addition, our framework provides an end-to-end complete pipeline to learn, and test, any deep RL controller for UAS or any three-axis robot for various control tasks. Our framework facilitates bi-directional iterative improvements to the simulation environment and real robot, by allowing instant deployment of the simulation learned controller to the real robot for performance verification and issue identification. Our results show that we can perform a zero-shot transfer of the RL agent, which is trained in simulation, to real CNC. The accuracy and precision do not meet the requirement for complex leaf sampling tasks yet. However, the RL agent trained for a static target following still follows or attempts to follow more dynamic and changing targets with predictable performance. This works lays the foundation by setting up the initial validation requirements for the leaf sampling tasks and identifies potential areas for improvement. Further tuning of the system and experimentation of the RL agent type would pave the way to autonomous aerial leaf sampling. Adviser: Carrick Detweile

A Person-Centric Design Framework for At-Home Motor Learning in Serious Games

abstract: In motor learning, real-time multi-modal feedback is a critical element in guided training. Serious games have been introduced as a platform for at-home motor training due to their highly interactive and multi-modal nature. This dissertation explores the design of a multimodal environment for at-home training in which an autonomous system observes and guides the user in the place of a live trainer, providing real-time assessment, feedback and difficulty adaptation as the subject masters a motor skill. After an in-depth review of the latest solutions in this field, this dissertation proposes a person-centric approach to the design of this environment, in contrast to the standard techniques implemented in related work, to address many of the limitations of these approaches. The unique advantages and restrictions of this approach are presented in the form of a case study in which a system entitled the "Autonomous Training Assistant" consisting of both hardware and software for guided at-home motor learning is designed and adapted for a specific individual and trainer. In this work, the design of an autonomous motor learning environment is approached from three areas: motor assessment, multimodal feedback, and serious game design. For motor assessment, a 3-dimensional assessment framework is proposed which comprises of 2 spatial (posture, progression) and 1 temporal (pacing) domains of real-time motor assessment. For multimodal feedback, a rod-shaped device called the "Intelligent Stick" is combined with an audio-visual interface to provide feedback to the subject in three domains (audio, visual, haptic). Feedback domains are mapped to modalities and feedback is provided whenever the user's performance deviates from the ideal performance level by an adaptive threshold. Approaches for multi-modal integration and feedback fading are discussed. Finally, a novel approach for stealth adaptation in serious game design is presented. This approach allows serious games to incorporate motor tasks in a more natural way, facilitating self-assessment by the subject. An evaluation of three different stealth adaptation approaches are presented and evaluated using the flow-state ratio metric. The dissertation concludes with directions for future work in the integration of stealth adaptation techniques across the field of exergames.Dissertation/ThesisDoctoral Dissertation Computer Science 201

A framework for experimental determination of localised vertical pedestrian forces on full-scale structures using wireless attitude and heading reference systems

This is the author accepted manuscript. The final version is available from Elsevier via the DOI in this record.A major weakness among loading models for pedestrians walking on flexible structures proposed in recent years is the various uncorroborated assumptions made in their development. This applies to spatio- temporal characteristics of pedestrian loading and the nature of multi-object interactions. To alleviate this problem, a framework for the determination of localised pedestrian forces on full-scale structures is presented using a wireless attitude and heading reference systems (AHRS). An AHRS comprises a triad of tri-axial accelerometers, gyroscopes and magnetometers managed by a dedicated data processing unit, allowing motion in three-dimensional space to be reconstructed. A pedestrian loading model based on a single point inertial measurement from an AHRS is derived and shown to perform well against benchmark data collected on an instrumented treadmill. Unlike other models, the current model does not take any predefined form nor does it require any extrapolations as to the timing and amplitude of pedestrian loading. In order to assess correctly the influence of the moving pedestrian on behaviour of a structure, an algorithm for tracking the point of application of pedestrian force is developed based on data from a single AHRS attached to a foot. A set of controlled walking tests with a single pedestrian is conducted on a real footbridge for validation purposes. A remarkably good match between the measured and simulated bridge response is found, indeed confirming applicability of the proposed framework.The research presented here was funded by EPSRC (grant EP/I029567/2). Authors thank Devon County Council for permitting the experimental campaign to be conducted on Baker Bridge in Exeter, UK, and Dr Erfan Shahabpour (supported by EPSRC grant EP/K03877X/1) for providing access to and assisting with measurements on the ADAL-3D treadmill at the University of Sheffield (funded by EPSRC grant EP/E018734/1)

Vibration serviceability of floors subjected to footfall loading of single and multiple occupants

There is an increasing number of modern floors designed according to current vibration serviceability design guidelines failing to provide satisfactory vibration serviceability performance. This is because the design guidelines are based on assumptions and knowledge that were available in the late 1990s and at the beginning of the 21st century. Since then, there has been developments in the construction trends towards lightweight and modular structures. Numerous number of studies were conducted in the last few years to improve design tools related to vibration serviceability of floors. However, there are still gaps where the realism of these models and design tools can be improved. This thesis aims to improve the realism of design tools related to footfall-induced vibration of floors based on the usage of floors. An improved method to take into account the influence of dynamic interaction between walking individuals and lightweight floors on the vibration response calculations is proposed. For floors in sensitive facilities, an improved model to predict vibration levels for any probability of exceedance is derived. This model is suitable for single person walking scenario which is relevant for such floors. A model for multiple pedestrian walking scenario is also developed to be utilised for other types of floors where this walking scenario is more likely to occur. To derive such a model, an advanced Ultra-WideBand location tracking system was utilised to collect data regarding people’s occupancy and movements on floors. This model was utilised to develop two approaches to predict vibration levels using a simplified method and a more comprehensive framework which includes full simulation of people’s movements and their corresponding vibration responses

Curricular Object Manipulation in LiDAR-based Object Detection

This paper explores the potential of curriculum learning in LiDAR-based 3D object detection by proposing a curricular object manipulation (COM) framework. The framework embeds the curricular training strategy into both the loss design and the augmentation process. For the loss design, we propose the COMLoss to dynamically predict object-level difficulties and emphasize objects of different difficulties based on training stages. On top of the widely-used augmentation technique called GT-Aug in LiDAR detection tasks, we propose a novel COMAug strategy which first clusters objects in ground-truth database based on well-designed heuristics. Group-level difficulties rather than individual ones are then predicted and updated during training for stable results. Model performance and generalization capabilities can be improved by sampling and augmenting progressively more difficult objects into the training samples. Extensive experiments and ablation studies reveal the superior and generality of the proposed framework. The code is available at https://github.com/ZZY816/COM.Comment: Accepted by CVPR 2023. The code is available at https://github.com/ZZY816/CO

A framework for experimental determination of localised vertical pedestrian forces on full-scale structures using wireless attitude and heading reference systems

A major weakness among loading models for pedestrians walking on flexible structures proposed in recent years is the various uncorroborated assumptions made in their development. This applies to spatio-temporal characteristics of pedestrian loading and the nature of multi-object interactions. To alleviate this problem, a framework for the determination of localised pedestrian forces on full-scale structures is presented using a wireless attitude and heading reference systems (AHRS). An AHRS comprises a triad of tri-axial accelerometers, gyroscopes and magnetometers managed by a dedicated data processing unit, allowing motion in three-dimensional space to be reconstructed. A pedestrian loading model based on a single point inertial measurement from an AHRS is derived and shown to perform well against benchmark data collected on an instrumented treadmill. Unlike other models, the current model does not take any predefined form nor does it require any extrapolations as to the timing and amplitude of pedestrian loading. In order to assess correctly the influence of the moving pedestrian on behaviour of a structure, an algorithm for tracking the point of application of pedestrian force is developed based on data from a single AHRS attached to a foot. A set of controlled walking tests with a single pedestrian is conducted on a real footbridge for validation purposes. A remarkably good match between the measured and simulated bridge response is found, indeed confirming applicability of the proposed framework