Computer Vision and Image Understanding
Face detection and tracking using a Boosted Adaptive Particle Filter
Journal of Visual Communication and Image Representation
3D Human Motion Tracking Using Progressive Particle Filter
ISVC '08 Proceedings of the 4th International Symposium on Advances in Visual Computing, Part II
Event analysis based on multiple interactive motion trajectories
IEEE Transactions on Circuits and Systems for Video Technology
3D human motion tracking based on a progressive particle filter
Pattern Recognition
IEEE Transactions on Image Processing
Trajectory-based representation of human actions
ICMI'06/IJCAI'07 Proceedings of the ICMI 2006 and IJCAI 2007 international conference on Artifical intelligence for human computing
Integrating the projective transform with particle filtering for visual tracking
Journal on Image and Video Processing - Special issue on advanced video-based surveillance
Editors Choice Article: Tracking highly correlated targets through statistical multiplexing
Image and Vision Computing
A compact association of particle filtering and kernel based object tracking
Pattern Recognition
MMM'10 Proceedings of the 16th international conference on Advances in Multimedia Modeling
Backtracking: Retrospective multi-target tracking
Computer Vision and Image Understanding
Multiple object tracking via prediction and filtering with a sobolev-type metric on curves
ECCV'12 Proceedings of the 12th international conference on Computer Vision - Volume Part I
Multi-constraints face detect-track system
CISIM'12 Proceedings of the 11th IFIP TC 8 international conference on Computer Information Systems and Industrial Management
Tracking-by-detection of multiple persons by a resample-move particle filter
Machine Vision and Applications
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A new particle filter, Kernel Particle Filter (KPF), is proposed for visual tracking for multiple objects in image sequences. The KPF invokes kernels to form a continuous estimate of the posterior density function and allocates particles based on the gradient derived from the kernel density estimate. A data association technique is also proposed to resolve the motion correspondence ambiguities that arise when multiple objects are present. The data association technique introduces minimal amount of computation by making use of the intermediate results obtained in particle allocation. We show that KPF performs robust multiple object tracking with improved sampling efficiency.