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View article: Distributed Expectation Propagation for Multi-Object Tracking over Sensor Networks
Distributed Expectation Propagation for Multi-Object Tracking over Sensor Networks Open
In this paper, we present a novel distributed expectation propagation algorithm for multiple sensors, multiple objects tracking in cluttered environments. The proposed framework enables each sensor to operate locally while collaboratively …
View article: Distributed Expectation Propagation for Multi-Object Tracking over Sensor Networks
Distributed Expectation Propagation for Multi-Object Tracking over Sensor Networks Open
In this paper, we present a novel distributed expectation propagation algorithm for multiple sensors, multiple objects tracking in cluttered environments. The proposed framework enables each sensor to operate locally while collaboratively …
View article: Decentralised Variational Inference Frameworks for Multi-object Tracking on Sensor Networks: Additional Notes
Decentralised Variational Inference Frameworks for Multi-object Tracking on Sensor Networks: Additional Notes Open
This paper tackles the challenge of multi-sensor multi-object tracking by proposing various decentralised Variational Inference (VI) schemes that match the tracking performance of centralised sensor fusion with only local message exchanges…
View article: DR-Pose: A Two-stage Deformation-and-Registration Pipeline for Category-level 6D Object Pose Estimation
DR-Pose: A Two-stage Deformation-and-Registration Pipeline for Category-level 6D Object Pose Estimation Open
Category-level object pose estimation involves estimating the 6D pose and the 3D metric size of objects from predetermined categories. While recent approaches take categorical shape prior information as reference to improve pose estimation…
View article: Variational Tracking and Redetection for Closely-spaced Objects in Heavy Clutter: Supplementary Materials
Variational Tracking and Redetection for Closely-spaced Objects in Heavy Clutter: Supplementary Materials Open
The non-homogeneous Poisson process (NHPP) is a widely used measurement model that allows for an object to generate multiple measurements over time. However, it can be difficult to efficiently and reliably track multiple objects under this…
View article: Consensus-based Distributed Variational Multi-object Tracker in Multi-Sensor Network
Consensus-based Distributed Variational Multi-object Tracker in Multi-Sensor Network Open
The growing need for accurate and reliable tracking systems has driven significant progress in sensor fusion and object tracking techniques. In this paper, we design two variational Bayesian trackers that effectively track multiple targets…
View article: An Adaptive and Scalable Multi-Object Tracker Based on the Non-Homogeneous Poisson Process
An Adaptive and Scalable Multi-Object Tracker Based on the Non-Homogeneous Poisson Process Open
This paper proposes a new adaptive framework for tracking multiple objects in the presence of data association uncertainty and heavy clutter, either with or without knowledge of the measurement rates and/or target shapes. Built upon an onl…
View article: Lévy State-Space Models for Tracking and Intent Prediction of Highly Maneuverable Objects
Lévy State-Space Models for Tracking and Intent Prediction of Highly Maneuverable Objects Open
In this paper, we present a Bayesian framework for manoeuvring object tracking and intent prediction using novel α-stable Lévy state-space models, expressed in continuous time as Lévy processes. In contrast to conventional (fully) Gaussian…
View article: Modeling intent and destination prediction within a Bayesian framework: Predictive touch as a usecase
Modeling intent and destination prediction within a Bayesian framework: Predictive touch as a usecase Open
In various scenarios, the motion of a tracked object, for example, a pointing apparatus, pedestrian, animal, vehicle, and others, is driven by achieving a premeditated goal such as reaching a destination. This is albeit the various possibl…
View article: Bayesian Intent Prediction for Fast Maneuvering Objects using Variable Rate Particle Filters
Bayesian Intent Prediction for Fast Maneuvering Objects using Variable Rate Particle Filters Open
The motion of a tracked object often has long term underlying dependencies due to premeditated actions dictated by intent, such as destination. Revealing this intent, as early as possible, can enable advanced intelligent system functionali…
View article: On Destination Prediction Based on Markov Bridging Distributions
On Destination Prediction Based on Markov Bridging Distributions Open
This letter presents an alternative, more consistent, construction for bridging distributions, which enables inferring the destination of a tracked object from the available partial sensory observations. Two algorithms are then introduced …