Tracking multiple moving targets with a mobile robot using particle filters and statistical data association

By Dirk Schulz, Wolfram Burgard, Dieter Fox, Armin Bernd Cremers. Proceedings 2001 ICRA. IEEE International Conference on Robotics and Automation (Cat. No.01CH37164), 2001

456 citations recorded by the source corpus.

Subfields: Artificial Intelligence, Computer Vision and Pattern Recognition

View Paper: https://www.semanticscholar.org/paper/29a363d3734f31ee35d77f6d516767b7c120bd15

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Citations in the corpus

Online simultaneous localization and mapping with detection and tracking of moving objects: theory and results from a ground vehicle in crowded urban areas (2003, unknown): “In indoor environments, the most important targets of the DATMO problem are people [6] [8] [9] [11] [16].”
Model-free detection and tracking of dynamic objects with 2D lidar (2015, background): “The work by Schulz et al. (2001) is similar in that they also identify legs of people in 2D range scans and apply the joint probabilistic data association filter (JPDAF) for robust tracking.”
Particle Filters in Robotics (2002, background): “Particle filters were able to solve two important, previously unsolved problems known as the global local ization [2] and the kidnapped robot [ 14] problems, in which a robot has to recover its pose under global uncertainty.”
Object Detection and Tracking for Autonomous Navigation in Dynamic Environments (2010, methodology): “1983), a particle filter (Isard and Blake 1998), or combinations thereof (Schulz et al. 2001).”
Markov Chain Monte Carlo Data Association for Multi-Target Tracking (2009, methodology): “5Recently, JPDA has also been applied to nonlinear problems using a particle filter [37].”
Monte Carlo filtering for multi target tracking and data association (2005, methodology): “The first, which we refer to as the Monte Carlo joint probabilistic data association filter (MC-JPDAF), is a generalisation of the strategy proposed in [1] and [2] to multiple observers and arbitrary proposal distributions.”
Conditional particle filters for simultaneous mobile robot localization and people-tracking (2002, methodology): “The feature-based people-tracking literature usually overcomes this proble m by tracking individual people using separate filters [3].”
An MCMC-Based Particle Filter for Tracking Multiple Interacting Targets (2004, background): “Recently, a particle ﬁltering version of the JPDAF has been proposed in [27].”
Sequential Monte Carlo methods for multiple target tracking and data fusion (2002, methodology): “Independently of the two latter works [6] and [29], we have developed the MTPF, where the data association is approached in the same probabilistic spirit as the basic PMHT [12], [32].”
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].”
Adapting the Sample Size in Particle Filters Through KLD-Sampling (2003, background): “2002a), people tracking (Schulz et al. 2001; Montemerlo, Thrun, and Whittaker 2002b), and fault detection (Verma, Langford, and Simmons 2001; de Freitas 2002).”
Simultaneous Localization, Mapping and Moving Object Tracking (2007, methodology): “In the tracking literature, there are a number of techniques for solving data association in the cluttered such as multiple hypothesis tracking (MHT) approaches (Reid, 1979; Cox and Hingorani, 1996) and joint probabilistic data association (JPDA) approaches (Fortmann et al., 1983; Schulz et al., 200”
FastSLAM: a factored solution to the simultaneous localization and mapping problem (2002, background): “Particle filters have been applied successfully to a variety of real world estimation problems [15, 28, 52].”
Natural person-following behavior for social robots (2007, unknown): “Our tracking approach is most similar to that of Topp et al [20], who use a single laser rangefinder and sample-based joint probabilistic data association filters to identify and track people [17].”
MCMC-based particle filtering for tracking a variable number of interacting targets (2005, methodology): “These multitarget tracking algorithms have been used extensively in the context of computer vision, e.g., nearest neighbor tracking in [5], the MHT in [6], and the JPDAF in [7] and [8].”
Robotic mapping: a survey (2003, background): “Such algorithms are popular in the target tracking literature [4, 68, 85].”
Tracking multiple objects with particle filtering (2002, background): “Lately, in mobile robotic [15], a set of particle filters for each target connected by a statistical data association has been proposed.”