Fundamentals of Object Tracking

By S. Challa, M. Morelande, D. Musicki, R. Evans. 2011

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Lidar for Autonomous Driving: The Principles, Challenges, and Trends for Automotive Lidar and Perception Systems (2020, background): “jects through spatio-temporal consistency. MOT maintains the identities of detected objects and yields their physical states i.e. trajectories, poses, velocities. MOT is a classic engineering problem [31] that has been researched for a long time. A basic architecture mainly comprises single object t”
Bayesian Filtering and Smoothing (2013, methodology): “Target tracking (Bar-Shalom et al., 2001; Crassidis and Junkins, 2004; Challa et al., 2011) refers to the methodology where a set of sensors such as active or passive radars, radio frequency sensors, acoustic arrays,”
Marginal multi-bernoulli filters: RFS derivation of MHT, JIPDA, and association-based member (2012, methodology): “The effect of coalescence is clearly visible from t = 100 to the end of the simulation for JITS, TOMB/P and LMITS.”