IEEE Transactions on Pattern Analysis and Machine Intelligence
Particle filters for maneuvering target tracking problem
Signal Processing
Robust target detection and tracking through integration of motion, color, and geometry
Computer Vision and Image Understanding
ACM Computing Surveys (CSUR)
Transductive local exploration particle filter for object tracking
Image and Vision Computing
EURASIP Journal on Applied Signal Processing
Adaptive multi-modal stereo people tracking without background modelling
Journal of Visual Communication and Image Representation
Pattern Recognition Letters
Multi-target tracking for flower counting using adaptive motion models
Computers and Electronics in Agriculture
Optimal recursive clustering of likelihood functions for multiple object tracking
Pattern Recognition Letters
Multi-camera people tracking using evidential filters
International Journal of Approximate Reasoning
People detection and tracking with multiple stereo cameras using particle filters
Journal of Visual Communication and Image Representation
Sequential particle generation for visual tracking
IEEE Transactions on Circuits and Systems for Video Technology
A distributed dynamical scheme for fastest mixing Markov chains
ACC'09 Proceedings of the 2009 conference on American Control Conference
Particle filtering with multiple and heterogeneous cameras
Pattern Recognition
Multi-object tracking based on a modular knowledge hierarchy
ICVS'03 Proceedings of the 3rd international conference on Computer vision systems
Multi-target tracking with poisson processes observations
PSIVT'07 Proceedings of the 2nd Pacific Rim conference on Advances in image and video technology
Face tracking using multiple facial features based on particle filter
CAR'10 Proceedings of the 2nd international Asia conference on Informatics in control, automation and robotics - Volume 3
Joint detection and estimation of multiple objects from image observations
IEEE Transactions on Signal Processing
Video object contour tracking using improved dual-front active contour
ICIC'06 Proceedings of the 2006 international conference on Intelligent computing: Part II
A stochastic graph evolution framework for robust multi-target tracking
ECCV'10 Proceedings of the 11th European conference on Computer vision: Part I
A hierarchical estimator for object tracking
EURASIP Journal on Advances in Signal Processing - Special issue on video analysis for human behavior understanding
Introducing fuzzy spatial constraints in a ranked partitioned sampling for multi-object tracking
ISVC'10 Proceedings of the 6th international conference on Advances in visual computing - Volume Part I
Detection and tracking of multiple similar objects based on color-pattern
AIS'11 Proceedings of the Second international conference on Autonomous and intelligent systems
People tracking algorithm for human height mounted cameras
DAGM'11 Proceedings of the 33rd international conference on Pattern recognition
Editors Choice Article: Tracking highly correlated targets through statistical multiplexing
Image and Vision Computing
Stochastic Optimization of Sensor Placement for Diver Detection
Operations Research
Robust decentralized multi-model adaptive template tracking
Pattern Recognition
Fuzzy spatial constraints and ranked partitioned sampling approach for multiple object tracking
Computer Vision and Image Understanding
Automatic tracking of a large number of moving targets in 3d
ECCV'12 Proceedings of the 12th European conference on Computer Vision - Volume Part IV
Low-complexity scalable distributed multicamera tracking of humans
ACM Transactions on Sensor Networks (TOSN)
Tracking-by-detection of multiple persons by a resample-move particle filter
Machine Vision and Applications
Persistent tracking of static scene features using geometry
Computer Vision and Image Understanding
Hi-index | 35.69 |
The classical particle filter deals with the estimation of one state process conditioned on a realization of one observation process. We extend it here to the estimation of multiple state processes given realizations of several kinds of observation processes. The new algorithm is used to track with success multiple targets in a bearings-only context, whereas a JPDAF diverges. Making use of the ability of the particle filter to mix different types of observations, we then investigate how to join passive and active measurements for improved tracking