Unmanned aerial vehicle (UAV) localization is typically performed through sensor fusion to leverage the complementary strengths of multiple sensors. However, establishing reliable localization redundancy with a single sensor remains fundamentally important for system reliability. In particular, external forces during flight, such as collisions, wind effects, or payload changes, induce significant discrepancies between control commands and the actual motion, making robust localization an inherently challenging task. This study explores a simultaneous localization and calibration (SLAC) algorithm for UAVs that improves localization accuracy by directly estimating the unknown external disturbances and compensating for their effects. A finite-memory SLAC (FM-SLAC) algorithm uses only recent information from a finite interval and has shown strong robustness in two-dimensional localization for mobile robots. However, in three-dimensional (3D) UAV localization, it exhibited numerical instability during the computation of the large matrix inversions included in the filter gain. To overcome this drawback, this study proposes a novel unscented information-form finite-memory approach for SLAC (UIFM-SLAC). By employing an unscented transformation (UT), an unscented finite-memory SLAC (UFM-SLAC) is developed to avoid the calculation of the Jacobian matrix and the inverse of large matrices required in the conventional FM-SLAC. Furthermore, an information form is integrated into the framework to establish the UIFM-SLAC, which reduces dependence on initial estimates, thereby eliminating numerical instabilities in high-dimensional models and mitigating accuracy loss caused by the linearization of nonlinear dynamics. Experimental comparisons with standard and state-of-the-art algorithms for robust UAV localization were conducted, and the results demonstrated the superior accuracy and robustness of the UIFM-SLAC.

This paper presents a novel approach to path planning for multi-platform bearings-only tracking of a target in the presence of epistemic detection uncertainty. Instead of using the traditional probabilistic framework, we propose a solution with a coordinated intelligent sensor platform motion control strategy in the possibilistic framework, offering a viable and robust alternative for improved tracking performance. We use track fusion of possibilistic Bernoulli filter implemented with Gaussian-max models to integrate information gathered by multiple platforms. The reward function for intelligent platform motion control is constructed using the concept of possibilistic entropy. The tracking performance of the proposed solution is evaluated using selected metrics via simulations.

Recent advancements have sparked a growing interest in integrating spatiotemporal analysis with large-scale language models. However, spatiotemporal data often contains sensitive information, making it unsuitable for open third-party access. To address this challenge, we propose a Graph-GAN-based model for generating privacy-protected spatiotemporal data. Our approach incorporates spatial and temporal attention blocks in the discriminator and a spatiotemporal deconvolution structure in the generator. These enhancements enable efficient training under Gaussian noise to achieve differential privacy. Extensive experiments conducted on three real-world spatiotemporal datasets validate the efficacy of our model. Our method provides a privacy guarantee while maintaining the data utility. The prediction model trained on our generated data maintains a competitive performance compared to the model trained on the original data.

We conduct a survey on drones used as a service, denoted as drone-as-a-service (DaaS). We develop a novel taxonomy based on DaaS functions, research tasks, and application domains. We provide a discussion on drones and their associated capabilities based on their type of use. We propose a three-layered DaaS system architecture that vertically integratescloudcomputing,drones, andservicesas a reference framework to compare existing drone service implementations. Additionally, we propose a representative uncertainty-aware DaaS model for delivery scenarios, illustrating how service definitions can incorporate both functional and nonfunctional attributes under dynamic environmental conditions. Finally, we identify and discuss future research directions and open problems related to the use of drones for service delivery.

Consider the problem of maritime surveillance using a high-resolution airborne radar for the detection and tracking of small surface targets. This is a challenging problem as the sea clutter is spiky with a non-Gaussian amplitude distribution and contains both temporally and spatially varying characteristics. As a possible solution, we have recently proposed a Bayesian track-before-detect algorithm, which assumes a compound K-distributed clutter model with Swerling 1 target fluctuations. This paper considers a suitable modification of this algorithm to work in the range-Doppler domain and evaluates its performance on real datasets collected by the Defence Science and Technology Group’s (DSTG) Ingara X-band radar.

The paper formulates the solution to area search for targets in the framework of possibility theory. The rationale is that the required measurement model parameters, such as the probability of detection and/or the probability of false alarm, are rarely known as precise values. Possibility theory was developed for quantitative modelling of and reasoning with epistemic uncertainty. It provides an elegant Bayesian like solution to target area search. A reward function is proposed as an uncertainty measure which takes into account the epistemic uncertainty. The robustness of the proposed search algorithm is demonstrated by numerical results.

An extended object in target tracking refers to the object which produces a time-varying number of noisy detections (measurements) from its scattering or feature points. The optimal sequential Bayesian state estimator for an appearing/disappearing extended object in the presence of false and missed detections is known as the Bernoulli Filter Ext (BF-X) [1]. Bayesian estimation methods rely on probabilistic models. When probabilistic models are known only partially or imprecisely, quantitative modeling of uncertainty can be carried out using possibility functions. This paper formulates the analog of the BF-X in the framework of possibility theory, where uncertainty is represented using possibility functions, rather than probability distributions. Possibility functions have the capacity to model with integrity the partial or imprecise probabilistic specifications and thus the proposed possibilistic BF-X is characterised by an enhanced robustness in the absence of precise measurement or dynamic models.

BACKGROUND: Automatic cell tracking methods enable practitioners to analyze cell behaviors efficiently. Notwithstanding the continuous development of relevant software, user-friendly visualization tools have room for further improvements. Typical visualization mostly comes with main cell tracking tools as a simple plug-in, or relies on specific software/platforms. Although some tools are standalone, limited visual interactivity is provided, or otherwise cell tracking outputs are partially visualized. RESULTS: This paper proposes a self-reliant visualization system, CellTrackVis, to support quick and easy analysis of cell behaviors. Interconnected views help users discover meaningful patterns of cell motions and divisions in common web browsers. Specifically, cell trajectory, lineage, and quantified information are respectively visualized in a coordinated interface. In particular, immediate interactions among modules enable the study of cell tracking outputs to be more effective, and also each component is highly customizable for various biological tasks. CONCLUSIONS: CellTrackVis is a standalone browser-based visualization tool. Source codes and data sets are freely available at http://github.com/scbeom/celltrackvis with the tutorial at http://scbeom.github.io/ctv_tutorial .

This paper proposes an online multi-camera multi-object tracker that only requires monocular detector training, independent of the multi-camera configurations, allowing seamless extension/deletion of cameras without retraining effort. The proposed algorithm has a linear complexity in the total number of detections across the cameras, and hence scales gracefully with the number of cameras. It operates in the 3D world frame, and provides 3D trajectory estimates of the objects. The key innovation is a high fidelity yet tractable 3D occlusion model, amenable to optimal Bayesian multi-view multi-object filtering, which seamlessly integrates, into a single Bayesian recursion, the sub-tasks of track management, state estimation, clutter rejection, and occlusion/misdetection handling. The proposed algorithm is evaluated on the latest WILDTRACKS dataset, and demonstrated to work in very crowded scenes on a new dataset.

We propose an uncertainty-aware service approach to provide drone-based delivery services called Drone-as-a-Service (DaaS) effectively. Specifically, we propose a service model of DaaS based on the dynamic spatiotemporal features of drones and their in-flight contexts. The proposed DaaS service approach consists of three components: scheduling, route-planning, and composition. First, we develop a DaaS scheduling model to generate DaaS itineraries through a Skyway network. Second, we propose anuncertainty-aware DaaS route-planning algorithmthat selects the optimal Skyways under weather uncertainties. Third, we develop two DaaS composition techniques to select an optimal DaaS composition at each station of the planned route. Aspatiotemporal DaaS composerfirst selects the optimal DaaSs based on their spatiotemporal availability and drone capabilities. Apredictive DaaS composerthen utilises the outcome of the first composer to enable fast and accurate DaaS composition using several Machine Learning classification methods. We train the classifiers using a new set of spatiotemporal features which are in addition to other DaaS QoS properties. Our experiments results show the effectiveness and efficiency of the proposed approach.

We present DroTrack, a high-speed visual single-object tracking framework for drone-captured video sequences. Most of the existing object tracking methods are designed to tackle well-known challenges, such as occlusion and cluttered backgrounds. The complex motion of drones, i.e., multiple degrees of freedom in three-dimensional space, causes high uncertainty. The uncertainty problem leads to inaccurate location predictions and fuzziness in scale estimations. DroTrack solves such issues by discovering the dependency between object representation and motion geometry. We implement an effective object segmentation based on Fuzzy C Means (FCM). We incorporate the spatial information into the membership function to cluster the most discriminative segments. We then enhance the object segmentation by using a pre-trained Convolution Neural Network (CNN) model. DroTrack also leverages the geometrical angular motion to estimate a reliable object scale. We discuss the experimental results and performance evaluation using two datasets of 51,462 drone-captured frames. The combination of the FCM segmentation and the angular scaling increased DroTrack precision by up to 9% and decreased the centre location error by 162 pixels on average. DroTrack outperforms all the high-speed trackers and achieves comparable results in comparison to deep learning trackers. DroTrack offers high frame rates up to 1000 frame per second (fps) with the best location precision, more than a set of state-of-the-art real-time trackers.

In the past few years, the performance of road defect detection has been remarkably improved thanks to advancements in various studies on computer vision and deep learning. Although large-scale and well-annotated datasets enhance the performance of detecting road defects to some extent, it is still challengeable to derive a model which can perform reliably for various road conditions in practice, because it is intractable to construct a dataset considering diverse road conditions and defect patterns. To end this, we propose an unsupervised approach to detect road defects, using Adversarial Image-to-Frequency Transform (AIFT). AIFT adopts the unsupervised manner and adversarial learning in deriving the defect detection model, so AIFT does not require annotations for road defects. We evaluate the efficiency of AIFT using GAPs384 dataset, Cracktree200 dataset, CRACK500 dataset, and CFD dataset. The experimental results demonstrate that the proposed approach detects various road detects, and it outperforms existing state-of-the-art approaches.

This paper proposes a robust multi-target tracking algorithm for uncertainty in dynamic motion modeling. To address this issue, the multi-target tracking problem is formulated under random finite set (RFS) framework with finite length memory filtering called receding horizon estimation (RHE). The proposed algorithm is based on the generalized labeled multi-Bernoulli (GLMB) filter which enables RHE for multi-target tracking. The proposed algorithm, a Receding Horizon GLMB (RH-GLMB) filter, is evaluated through a numerical example and visual tracking datasets where dynamic modeling uncertainty exists.

In multi‐object tracking applications, model parameter tuning is a prerequisite for reliable performance. In particular, it is difficult to know statistics of false measurements due to various sensing conditions and changes in the field of views. In this study, the authors are interested in designing a multi‐object tracking algorithm that handles unknown false measurement rate. The recently proposed robust multi‐Bernoulli filter is employed for clutter estimation while generalised labelled multi‐Bernoulli filter is considered for target tracking. Performance evaluation with real videos demonstrates the effectiveness of the tracking algorithm for real‐world scenarios.

In this paper, we propose a novel multi-object tracking method to track unknown number of objects with a single camera system. We design the tracking method via probability hypothesis density (PHD) filtering which considers multiple object states and their observations as random finite sets (RFSs). The PHD filter is capable of rejecting clutters, handling object appearances and disappearances, and estimating the trajectories of multiple objects in a unified framework. Although the PHD filter is robust to cluttered environment, it is vulnerable to missed detections. For this reason, we include local observations in an RFS of observation model. Local observations are locally generated near the individual tracks by using on-line trained local detector. The main purpose of the local observation is to handle the missed detections and to provide identity (label information) to each object in filtering procedure. The experimental results show that the proposed method robustly tracks multiple objects under practical situations.

In this paper, we propose an efficient and accurate visual tracker equipped with a new particle filtering algorithm and robust subspace learning-based appearance model. The proposed visual tracker avoids drifting problems caused by abrupt motion changes and severe appearance variations that are well-known difficulties in visual tracking. The proposed algorithm is based on a type of auxiliary particle filtering that uses a spatio-temporal sliding window. Compared to conventional particle filtering algorithms, spatio-temporal auxiliary particle filtering is computationally efficient and successfully implemented in visual tracking. In addition, a real-time robust principal component pursuit (RRPCP) equipped with l(1)-norm optimization has been utilized to obtain a new appearance model learning block for reliable visual tracking especially for occlusions in object appearance. The overall tracking framework based on the dual ideas is robust against occlusions and out-of-plane motions because of the proposed spatio-temporal filtering and recursive form of RRPCP. The designed tracker has been evaluated using challenging video sequences, and the results confirm the advantage of using this tracker.

In this paper, a computationally efficient algorithm for level set based tracking is suggested for near real-time implementation. The problem of computational complexity in level set based tracking is tackled by combining a sparse field level set method (SFLSM) with a Rao-Blackwellized particle filter (RBPF). Under the RBPF framework, affine motion is estimated using an appearance-based particle filtering (PF) to provide the initial curves for SFLSM and the local deformation of contours is analytically estimated through SFLSM. SFLSM is adopted to significantly reduce the computational complexity of the level set method (LSM) implementation. For the initial curve estimation in SFLSM, the estimated position and object scale are provided by the appearance-based PF in order to achieve the desired efficiency. Furthermore, the appearance-based PF alleviates inaccurate segmentation incurred by an incorrect initial curve. Experimental results with a real-video confirm the promising performance of this method.

The receding horizon estimation is applied to design robust visual trackers. Most recent data within the fixed size of windows is receding, and is processed to obtain an estimate of the object state at the current time. In visual tracking such a scheme improves filter accuracy by avoiding accumulated approximation errors. A newly derived unscented Kalman filter (UKF) based on the receding horizon strategy is proposed for determining the importance density of the hybrid particle filter. The importance density derived by the receding horizon-based UKF (RHUKF) provides significantly improved accuracy and performance consistency compared to the unscented particle filter (UPF). Visual tracking examples are subsequently tested to demonstrate the advantages of the filter.