People Tracking with Mobile Robots Using Sample-Based Joint Probabilistic Data Association Filters

By Dirk Schulz, Wolfram Burgard, Dieter Fox, Armin Bernd Cremers. Int. J. Robotics Res., 2003

512 citations recorded by the source corpus.

Subfields: Artificial Intelligence, Computer Vision and Pattern Recognition

View Paper: https://www.semanticscholar.org/paper/98b2f4094a692e44d440b2c3cb5a77d3f73ae228

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In Artificial Intelligence

In Computer Vision and Pattern Recognition

Citations in the corpus

Towards 3D object recognition via classification of arbitrary object tracks (2011, methodology): “in [9], [10], [11], [12], [13], but these methods require the explicit specification of new tracker models for each additional object class.”
HERB: a home exploring robotic butler (2010, background): “However, these approaches either use a static map, or seek to remove moving objects from the map ( Schulz et al. 2003; Hahnel et al. 2003).”
Using Boosted Features for the Detection of People in 2D Range Data (2007, background): “moving blobs that appear as local minima in the range image [1], [2], [3], [4].”
Multisensor-Based Human Detection and Tracking for Mobile Service Robots (2009, background): “Most of the approaches found in the literature for tracking persons with a mobile robot are based on the EKF [22], [23] or on particle filters [8], [9], [18].”
People detection in RGB-D data (2011, methodology): “Early works used 2D range data for this task [1], [2].”
Learning Motion Patterns of People for Compliant Robot Motion (2005, methodology): “Whenever new sensory input arrives we follow the idea of JPDAFs and integrate the single features according to the assignment probabilityλsr that feature s corresponds to person r (Schulz et al. 2003): P(ξ t r | z(1:t)) = η St ∑”
Situated Dialogue and Spatial Organization: What, Where… and Why? (2007, methodology): “In order to follow the tutor, we use a laser range based people tracking software (Schulz et al., 2003) that uses a Bayesian filtering algorithm.”
Monte Carlo filtering for multi target tracking and data association (2005, methodology): “The Monte Carlo implementation of the JPDAF presented in this section is a generalisation of the strategy proposed in [1] and [2] to multiple observers and arbitrary proposal distributions.”