Maintaining multimodality through mixture tracking

By Jaco Vermaak, Arnaud Doucet, Patrick Pérez. Proceedings Ninth IEEE International Conference on Computer Vision, 2003

471 citations recorded by the source corpus.

Subfields: Artificial Intelligence, Signal Processing

View Paper: https://www.semanticscholar.org/paper/34889e5bad5a48bbef230ffa0a2079e001b694dd

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Citations in the corpus

Robust localization and tracking of simultaneous moving sound sources using beamforming and particle filtering (2007, background): “These new capabilities allow a mobile robot to interact using more natur l means with people in real life settings.”
Robust tracking-by-detection using a detector confidence particle filter (2009, background): “Later extensions include a representation of the joint state space for multiple targets [24] and the combination with an object detector for Markovian tracking-by-detection [10, 20].”
Continuous Energy Minimization for Multitarget Tracking (2014, background): “Later, particle filtering (also known as sequential Monte Carlo) was introduced, where a set of weighted particles – sampled from a proposal distribution – is maintained to represent the current, hidden state [10], [30], [37].”
Unsupervised Detection and Tracking of Arbitrary Objects with Dependent Dirichlet Process Mixtures (2012, methodology): “An approach to this problem involving the use of a nonparametric mixture model has also found success in maintaining isolation of distinct objects [60].”
A Boosted Particle Filter: Multitarget Detection and Tracking (2004, methodology): “In the mixture approach of [17], the particles are used to ob ain the following approximation of the posterior distribution :”
MCMC-based particle filtering for tracking a variable number of interacting targets (2005, unknown): “of targets [21], [22].”
Multi-target tracking by continuous energy minimization (2011, background): “[5], and later particle filtering [8, 13, 16], which represents the posterior by a set of samples rather than an analytic expression, and can thus better cope with ambiguous, multi-modal distributions.”
Online Multiperson Tracking-by-Detection from a Single, Uncalibrated Camera (2011, background): “Other researchers extended the framework for multiple targets by either representing all targets jointly in a particle filter [ 43 ] or by extending the state space of each target to include components of other targets [41].”
Structured Pruning of Deep Convolutional Neural Networks (2015, methodology): “With a set of weighted particles, the PF represents the filtering distribution [Vermaak et al. 2003].”
Hierarchical Clustering of a Mixture Model (2004, background): “Other instances in which collapsing MoGs is relevant are variants of particle filtering [10], non-parametric belief propagation [7] and fault detection in dynamical systems [3].”
Multiple Object Tracking Using K-Shortest Paths Optimization (2011, unknown): “Particle filtering can address some of the limitations of Kalman filtering by exploring multiple hypotheses [13], [1], [14], [15], [16], [17].”