Markov Chain Monte Carlo Data Association for Multi-Target Tracking

By Songhwai Oh, Stuart J. Russell, S. Sastry. IEEE Transactions on Automatic Control, 2009

346 citations recorded by the source corpus.

View Paper: https://www.semanticscholar.org/paper/cc68f9bef556907ff74a6906eba6f128e6d99580

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In Artificial Intelligence

Citations in the corpus

Venture: a higher-order probabilistic programming platform with programmable inference (2014, background): “…(Manning and Schütze, 1999), speech recognition (Baker, 1979), information extraction (Pasula et al., 2002), multitarget tracking and sensor fusion (Oh et al., 2009; Arora et al., 2010), ecology (Csilléry et al., 2010) and computational biology (Friedman et al., 2000; Yu et al., 2004; Toni et…”
Online Multi-object Tracking Using CNN-Based Single Object Tracker with Spatial-Temporal Attention Mechanism (2017, methodology): “On the contrary, online methods generate trajectories only using information up to the current frame which adopt probabilistic inference [34] or deterministic optimization (e.”
Learning to Track: Online Multi-object Tracking by Decision Making (2015, methodology): “Majority of the batch methods ([43, 25, 31, 7, 36, 11, 27]) formulates MOT as a global optimization problem in a graph-based representation, while online methods solve the data association problem either probabilistically [33, 19, 32] or determinatively (e.”
Spatial-Temporal Relation Networks for Multi-Object Tracking (2019, background): “According to what kind of information is used to establishing the association between objects in different frames, the existing methods can be categorized into online methods [2, 12, 19, 27, 33, 35, 38, 47, 50, 58, 59, 9], and offline methods [7, 14, 36, 39, 40, 44, 51, 52, 53, 62].”
Joint Probabilistic Data Association Revisited (2015, methodology): “[31] proposed a more principled JPDA approximation based on Markov chain Monte Carlo (MCMC) data association.”
Multiple Hypothesis Tracking Revisited (2015, background): “MHT was known to be sensitive to its parameter settings [32].”
An Efficient Implementation of the Generalized Labeled Multi-Bernoulli Filter (2016, methodology): “Indeed, MCMC simulation has been applied to compute posterior distributions of data association variables in multi-object tracking problems [43].”
Deep Network Flow for Multi-object Tracking (2017, background): “ribe the path of individual object instances over time. Bipartite graph matching [25,35] is often employed in on-line approaches to assign bounding boxes in the current frame to existing trajectories [22,37,38,52]. Off-line methods can be elegantly formulated in a network ﬂow framework to solve the ”
Marginal multi-bernoulli filters: RFS derivation of MHT, JIPDA, and association-based member (2012, methodology): “Note that the filter derivation above is not tied to the LBP approximation; other methods such as Maskell's exact EHM [31] or MCMCDA [32] may also be used to calculate the marginal association probabilities.”