An MCMC-Based Particle Filter for Tracking Multiple Interacting Targets

By Zia Khan, Tucker R. Balch, Frank Dellaert. European Conference on Computer Vision, 2004

442 citations recorded by the source corpus.

View Paper: https://www.semanticscholar.org/paper/a06fe9dc2f1a26d513338d55b338814562703cc8

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In Artificial Intelligence

Citations in the corpus

A Linear Programming Approach for Multiple Object Tracking (2007, background): “Recently, particle filtering has been popular for tracking multiple objects such as ants [ 2 ] with complex interactions.”
Unsupervised Detection and Tracking of Arbitrary Objects with Dependent Dirichlet Process Mixtures (2012, methodology): “There has been a particular emphasis on developing ways to deal with problems such as object occlusion (where one object blocks another from the view of a video camera) [54, 18, 66], complex object interactions [38, 44, 19], objects with similar appearances [42, 36], variable (and potentially high) ”
Tracking the Untrackable: Learning to Track Multiple Cues with Long-Term Dependencies (2017, background): “, birds [46], ants [30], fish [69, 70, 16], cells [47, 41]), robot navFigure 1.”
Tracking Multiple Occluding People by Localizing on Multiple Scene Planes (2009, methodology): “For a detailed review of the state of the art in tracking research, the reader is referred to the recent survey by Yilmaz et al. [61].”
Decentralized multiple target tracking using netted collaborative autonomous trackers (2005, background): “Coalescence often takes place especially when the targets are close or present occlusions [9, 20].”
Multiple object tracking: A literature review (2014, background): “Objects to track can be, for example, pedestrians on the street [1], [2], vehicles in the road [3], [4], sport players on the court [5], [6], [7], or groups of animals (birds [8], bats [9], ants [10], fish [11], [12], [13], cells [14], [15], etc.”
Unsupervised Bayesian Detection of Independent Motion in Crowds (2006, methodology): “See [9] for interesting examples of using related techniques to track more sparse “crowds” of ants, or hockey players in the case of [15]’s impressive boosted particle filter.”