Tracking multiple speakers using CPHD filter
Proceedings of the 15th international conference on Multimedia
Gaussian mixture CPHD filter with gating technique
Signal Processing
Localization of multiple emitters based on the sequential PHD filter
Signal Processing
Sequential estimation of multipath MIMO-OFDM channels
IEEE Transactions on Signal Processing
The cardinality balanced multi-target multi-Bernoulli filter and its implementations
IEEE Transactions on Signal Processing
Gaussian mixture PHD filter and its application in multi-target tracking
CCDC'09 Proceedings of the 21st annual international conference on Chinese control and decision conference
The bin-occupancy filter and its connection to the PHD filters
IEEE Transactions on Signal Processing
IEEE Transactions on Signal Processing
Tracking random finite objects using 3D-LIDAR in marine environments
Proceedings of the 2010 ACM Symposium on Applied Computing
Probability hypothesis density approach for multi-camera multi-object tracking
ACCV'07 Proceedings of the 8th Asian conference on Computer vision - Volume Part I
Multi-target tracking with poisson processes observations
PSIVT'07 Proceedings of the 2nd Pacific Rim conference on Advances in image and video technology
A new tracking method for small infrared targets
ICIP'09 Proceedings of the 16th IEEE international conference on Image processing
Bayesian multi-object filtering with amplitude feature likelihood for unknown object SNR
IEEE Transactions on Signal Processing
Ant clustering PHD filter for multiple-target tracking
Applied Soft Computing
Cubature kalman filtering for continuous-discrete systems: theory and simulations
IEEE Transactions on Signal Processing
Joint detection and estimation of multiple objects from image observations
IEEE Transactions on Signal Processing
A linear multisensor PHD filter using the measurement dimension extension approach
ICSI'11 Proceedings of the Second international conference on Advances in swarm intelligence - Volume Part II
Visual tracking of multiple targets by multi-bernoulli filtering of background subtracted image data
ICSI'11 Proceedings of the Second international conference on Advances in swarm intelligence - Volume Part II
Mobile robotics in a random finite set framework
ICSI'11 Proceedings of the Second international conference on Advances in swarm intelligence - Volume Part II
A new method based on ant colony optimization for the probability hypothesis density filter
ICSI'11 Proceedings of the Second international conference on Advances in swarm intelligence - Volume Part II
Detection-guided multi-target Bayesian filter
Signal Processing
PHD filter based track-before-detect for MIMO radars
Signal Processing
Extensions of the SMC-PHD filters for jump Markov systems
Signal Processing
Laser and Radar Based Robotic Perception
Foundations and Trends in Robotics
A novel track maintenance algorithm for PHD/CPHD filter
Signal Processing
Dim target tracking base on GM-PHD filter
IScIDE'11 Proceedings of the Second Sino-foreign-interchange conference on Intelligent Science and Intelligent Data Engineering
Kalman particle PHD filter for multi-target visual tracking
IScIDE'11 Proceedings of the Second Sino-foreign-interchange conference on Intelligent Science and Intelligent Data Engineering
Multisensor data fusion: A review of the state-of-the-art
Information Fusion
ACM Transactions on Intelligent Systems and Technology (TIST) - Special section on twitter and microblogging services, social recommender systems, and CAMRa2010: Movie recommendation in context
Game-theoretical occlusion handling for multi-target visual tracking
Pattern Recognition
High-speed Sigma-gating SMC-PHD filter
Signal Processing
A multiple model probability hypothesis density tracker for time-lapse cell microscopy sequences
IPMI'13 Proceedings of the 23rd international conference on Information Processing in Medical Imaging
Hi-index | 35.71 |
A new recursive algorithm is proposed for jointly estimating the time-varying number of targets and their states from a sequence of observation sets in the presence of data association uncertainty, detection uncertainty, noise, and false alarms. The approach involves modelling the respective collections of targets and measurements as random finite sets and applying the probability hypothesis density (PHD) recursion to propagate the posterior intensity, which is a first-order statistic of the random finite set of targets, in time. At present, there is no closed-form solution to the PHD recursion. This paper shows that under linear, Gaussian assumptions on the target dynamics and birth process, the posterior intensity at any time step is a Gaussian mixture. More importantly, closed-form recursions for propagating the means, covariances, and weights of the constituent Gaussian components of the posterior intensity are derived. The proposed algorithm combines these recursions with a strategy for managing the number of Gaussian components to increase efficiency. This algorithm is extended to accommodate mildly nonlinear target dynamics using approximation strategies from the extended and unscented Kalman filters