Multiple Object Tracking in Unknown Backgrounds With Labeled Random Finite Sets

Abstract

This paper proposes an on-line multiple object tracking algorithm that can operate in unknown background. In a majority of multiple object tracking applications, model parameters for background processes such as clutter and detection are unknown and vary with time, hence the ability of the algorithm to adaptively learn the these parameters is essential in practice. In this work, we detail how the Generalized Labeled Multi-Bernouli (GLMB) filter, a tractable and provably Bayes optimal multi-object tracker, can be tailored to learn clutter and detection parameters on-the-fly while tracking. Provided that these background model parameters do not fluctuate rapidly compared to the data rate, the proposed algorithm can adapt to the unknown background yielding better tracking performance.

A Generalized Labelled Multi-Bernoulli Filter for Extended Targets With Unknown Clutter Rate and Detection Profile

“…In [19], a new filter to process objects with multiple kinematic model called multi-class GLMB is presented based on JMS theory. By using the kinematic model as a class label and assuming that targets with different models do not interact with each other, the problem of jointly tracking and classification can be addressed by applying Multi-Class GLMB recursion.…”

“…To jointly accommodate the unknown clutter rate and detection profile, the basic idea is to hybridize and augment the single extended target state. Similar to [19], clutter is assumed to be generated by another kind of target with no dynamics, i.e. clutter generator, and multi-class GLMB is applied to provide traceable solution of jointly tracking multiple targets with different kinematic models.…”

“…Nonetheless, none of these filters differentiate trajectories. After introducing labelled RFS method into this problem, an efficient implementation of GLMB for point targets with unknown background parameters is described in [19] and it shows better robustness in high clutter rate or low detection probability situations compared with CPHD filter in [17].…”

Multisensor RFS Filters for Unknown and Changing Detection Probability

“…Subsequently, a multitarget multi-Bernoulli filter [26,27] was proposed to adaptively learn the nonhomogeneous clutter intensity and detection probability. Meanwhile, a multitarget tracker based on the labeled RFS was proposed in [28], which can estimate the clutter rate and detection profile. All these filters assume that the detection probability is Beta-distributed and calculate the detection probability through the accumulation of observation effects in the BGM model.…”

Multi-Target Localization and Tracking Using TDOA and AOA Measurements Based on Gibbs-GLMB Filtering

“…GLMB RFS has been applied in many fields, such as tracking with merged measurements [39], extended targets [40], computer vision [41][42][43], cell tracking [44,45], track-before-detect [46,47], sensor scheduling [48,49], field robotics [50][51][52], distributed tracking [53,54] and cell microscopy [55]. The GLMB solution has also been applied to the multi-sensor case [56] and the multi-scan case [57].…”