Exploiting Bounded Sensor Field-of-View Geometry in Tracking and Sensor Planning Problems

In search-detect-track problems, knowledge of where objects were not seen can be as valuable as knowledge of where objects were seen. Exploiting the sensor's known sensing extents, or field-of-view (FoV), this type of evidence can be incorporated in a Bayesian framework to improve tracking accuracy and form better sensor schedules. This paper presents new techniques for incorporating bounded FoV inclusion/exclusion evidence in object state densities and multi-object cardinality distributions. Some examples of how the proposed techniques may be applied to tracking and sensor planning problems are given

Multi-Objective Multi-Agent Planning for Jointly Discovering and Tracking Mobile Object

We consider the challenging problem of online planning for a team of agents to autonomously search and track a time-varying number of mobile objects under the practical constraint of detection range limited onboard sensors. A standard POMDP with a value function that either encourages discovery or accurate tracking of mobile objects is inadequate to simultaneously meet the conflicting goals of searching for undiscovered mobile objects whilst keeping track of discovered objects. The planning problem is further complicated by misdetections or false detections of objects caused by range limited sensors and noise inherent to sensor measurements. We formulate a novel multi-objective POMDP based on information theoretic criteria, and an online multi-object tracking filter for the problem. Since controlling multi-agent is a well known combinatorial optimization problem, assigning control actions to agents necessitates a greedy algorithm. We prove that our proposed multi-objective value function is a monotone submodular set function; consequently, the greedy algorithm can achieve a (1-1/e) approximation for maximizing the submodular multi-objective function.Comment: Accepted for publication to the Thirty-Fourth AAAI Conference on Artificial Intelligence (AAAI-20). Added algorithm 1, background on MPOMDP and OSP

Multi-object tracking in video using labeled random finite sets

The safety of industrial mobile platforms (such as fork lifts and boom lifts) is of major concern in the world today as industry embraces the concepts of Industry 4.0. The existing safety methods are predominantly based on Radio Frequency Identification (RFID) technology and therefore can only determine the distance at which a pedestrian who is wearing an RFID tag is standing. Other methods use expensive laser scanners to map the surrounding and warn the driver accordingly. The aim of this research project is to improve the safety of industrial mobile platforms, by detecting and tracking pedestrians in the path of the mobile platform, using readily available cheap camera modules. In order to achieve this aim, this research focuses on multi-object tracking which is one of the most ubiquitously addressed problems in the field of \textit{Computer Vision}. Algorithms that can track targets under severe conditions, such as varying number of objects, occlusion, illumination changes and abrupt movements of the objects are investigated in this research project. Furthermore, a substantial focus is given to improving the accuracy and, performance and to handling misdetections and false alarms. In order to formulate these algorithms, the recently introduced concept of Random Finite Sets (RFS) is used as the underlying mathematical framework. The algorithms formulated to meet the above criteria were tested on standard visual tracking datasets as well as on a dataset which was created by our research group, for performance and accuracy using standard performance and accuracy metrics that are widely used in the computer vision literature. These results were compared with numerous state-of-the-art methods and are shown to outperform or perform favourably in terms of the metrics mentioned above

Methods for Online UAV Path Planning for Tracking Multiple Objects

Unmanned aerial vehicles (UAVs) or drones have rapidly evolved to enable carrying various sensors such as thermal sensors for vision or antennas for radio waves. Therefore, drones can be transformative for applications such as surveillance and monitoring because they have the capability to greatly reduce the time and cost associated with traditional tasking methods. Realising this potential necessitates equipping UAVs with the ability to perform missions autonomously. This dissertation considers the problems of online path planning for UAVs for the fundamental task of surveillance comprising of tracking and discovering multiple mobile objects in a scene. Tracking and discovering an unknown and time-varying number of objects is a challenging problem in itself. Objects such as people or wildlife tend to switch between various modes of movements. Measurements received by the UAV’s on-board sensors are often very noisy. In practice, the on-board sensors have a limited field of view (FoV), hence, the UAV needs to move within range of the mobile objects that are scattered throughout a scene. This is extremely challenging because neither the exact number nor locations of the objects of interest are available to the UAV. Planning the path for UAVs to effectively detect and track multi-objects in such environments poses additional challenges. Path planning techniques for tracking a single object are not applicable. Since there are multiple moving objects appearing and disappearing in the region, following only certain objects to localise them accurately implies that a UAV is likely to miss many other objects. Furthermore, online path planning for multi-UAVs remains challenging due to the exponential complexity of multi-agent coordination problems. In this dissertation, we consider the problem of online path planning for UAV-based localisation and tracking of multi-objects. First, we realised a low cost on-board radio receiver system on aUAV and demonstrated the capability of the drone-based platform for autonomously tracking and locating multiple mobile radio-tagged objects in field trials. Second, we devised a track-before-detect filter coupled with an online path planning algorithm for joint detection and tracking of radio-tagged objects to achieve better performance in noisy environments. Third, we developed a multi-objective planning algorithm for multi-agents to track and search multi-objects under the practical constraint of detection range limited on-board sensors (or FoV limited sensors). Our formulation leads to a multi-objective value function that is a monotone submodular set function. Consequently, it allows us to employ a greedy algorithm for effectively controlling multi-agents with a performance guarantee for tracking discovered objects while searching for undiscovered mobile objects under practical constraints of limited FoV sensors. Fourth, we devised a fast distributed tracking algorithm that can effectively track multi-objects for a network of stationary agents with different FoVs. This is the first such solution to this problem. The proposed method can significantly improve capabilities of a network of agents to track a large number of objects moving in and out of the limited FoV of the agents’ sensors compared to existing methods that do not consider the problem of unknown and limited FoV of sensors.Thesis (Ph.D.) -- University of Adelaide, School of Computer Science, 202

Multi-sensor control for multi-object Bayes filters

Sensor management in multi-object stochastic systems is a theoretically and computationally challenging problem. This paper presents a new approach to the multi-target multi-sensor control problem within the partially observed Markov decision process (POMDP) framework. We model the multi-object state as a labeled multi-Bernoulli random finite set (RFS), and use the labeled multi-Bernoulli filter in conjunction with minimizing a task-driven control objective function: posterior expected error of cardinality and state (PEECS). A major contribution is a guided search for multi-dimensional optimization in the multi-sensor control command space, using coordinate descent method. In conjunction with the Generalized Covariance Intersection method for multi-sensor fusion, a fast multi-sensor control algorithm is achieved. Numerical studies are presented in several scenarios where numerous controllable (mobile) sensors track multiple moving targets with different levels of observability. The results show that our method works significantly faster than the approach taken by the state of the art methods, with simi lar tracking errors

Optical based statistical space objects tracking for catalogue maintenance

The number of space objects has grown substantially in the past decades due to new launches, regular mission activities, and breakup events. This has significantly affected the space environment and the development of the space industry. To ensure safe operation of space assets, Space Situational Awareness (SSA) has attracted considerable attention in recent years. One primary strategy in SSA is to establish and maintain a Space Object Catalogue (SOC) to provide timely updated data for SSA applications, e.g., conjunction analysis, collision avoidance manoeuvring. This thesis investigates three techniques for SOC maintenance, namely the tracklet association method for initial orbit determination, the multi-target tracking method for the refinement of orbital state estimation, and multi-sensor tasking method for the optimisation of sensor resources. Generally speaking, due to the limited number of optical sensors used to track the large population of space objects, the obtained observational arcs for many targets are very short. Such short arcs, which contain a small number of angular observations, are referred as tracklets. Given such limited data, typical orbit determination methods, e.g., Laplace, Gaussian, Double-R methods, may fail to produce a valid orbital solution. By contrast, tracklet association methods compare and correlate multiple tracklets across time, and following successful association, a reliable initial orbital state can be further determined for SOC maintenance. This thesis proposes an improved initial value problem optimisation method for accurate and efficient tracklet association, and a common ellipse method to distinguish false associations of tracklets from objects in the same constellation. The proposed methods are validated using real optical data collected from the Mount Stromlo Observatory, Canberra, Australia. Furthermore, another challenging task in SSA is to track multiple objects for the maintenance of a catalog. The Bayesian multi-target tracking filter addresses this issue by associating measurements to initially known or newly detected targets and simultaneously estimating the timevarying number of targets and their orbital states. In order to achieve efficient tracking of the new space objects, a novel birth model using the Boundary Value Problem (BVP) approach is proposed. The proposed BVP birth model is implemented in the Labelled Multi-Bernoulli (LMB) filter, which is an efficient multi-target tracker developed based on the Random Finite Set (RFS) theory, for improved computational efficiency of new space object tracking. Simulation results indicate that the computational efficiency of the proposed method significantly outperforms the state-of-the-art methods. Finally, as limited sensors are available for SOC maintenance, an appropriate sensor tasking scheme is essential for the optimisation of sensor resources. The optimal sensor tasking command allocates multiple sensors to take the best action and produce useful measurements for more accurate orbital state estimation. In this thesis, an analytical form is derived for the Rényi divergence of LMB RFS in which each target state density is a single Gaussian component. The obtained analytical Rényi divergence is formulated as a reward function for multi-sensor tasking, which improves the computational efficiency, especially for large-scale space object tracking. In addition, this thesis further investigates the benefits of using the analytical Rényi  divergence and various space-based and ground-based sensor networks for accurate tracking of objects in geosynchronous Earth orbit