Particle Filters for Multiple Target Tracking

Jinan, Rooji; Raveendran, Tara

doi:10.1016/j.protcy.2016.05.215

Cited by 22 publications

(11 citation statements)

References 9 publications

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“…For reducing the joint association complexity, linear multi-target integrated probabilistic data association (LMIPDA) [12] is also developed. For handling nonlinear dynamics of multiple objects, sequential Monte Carlo (SMC) methods [13]- [16] for MOT are developed. To estimate object states and cardinality simultaneously, joint probabilistic probability densities of multiple objects are modeled in [13], [14].…”

Section: Related Workmentioning

confidence: 99%

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Online Multi-Object Tracking With Visual and Radar Features

Bae

2020

IEEE Access

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Multi-object tracking (MOT) constructs multiple object trajectories by associating detections between consecutive frames while maintaining object identities. In many autonomous systems equipped with a camera and a radar, an amplitude and visual features can be measured. Therefore, our goal is to solve a MOT problem by associating detections with both features. To achieve it, we propose a unified MOT framework based on object model learning and confidence-based association. For improving discriminability between different objects, we present a method to learn several visual and amplitude object models during online tracking. By applying the learned object models for the affinity evaluation, we improve the confidence-based association further. In addition, we present a practical track management method to initialize and terminate tracks, and eliminate duplicated false tracks. We implement several MOT systems with different object model learning and association methods, and compare our system with them on challenging visual MOT datasets. We further compare our method with the recent deep appearance learning methods. These comparisons verify that our method can achieve the competitive tracking accuracy while maintaining a low MOT complexity.

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Section: Related Workmentioning

confidence: 99%

“…However, the computational complexity of these methods increases exponentially as the number of hypotheses increases. To alleviate this problem, the data association and state estimation are treated as a separated problem in [15], [16].…”

Section: Related Workmentioning

confidence: 99%

Online Multi-Object Tracking With Visual and Radar Features

Bae

2020

IEEE Access

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“…However, the computational complexity increases exponentially when the number of particles increases. To mitigate the curse‐of‐dimensionality problem, the authors in [6–9] solve the data association and state estimation problems separately. Based the Rao‐Blackwellisation method, Särkkä et al [7] used particle filtering and Kalman filtering for data association and state estimation, respectively.…”

Section: Mtt Frameworkmentioning

confidence: 99%

“…In [8], a game‐theoretic framework is developed for deterministic association. Sample‐based joint probabilistic data association [9] is provided for applying a ensemble square root filter for MTT. The reader is kindly referred to [27, 28] for more details of particle filtering for MTT.…”

Section: Mtt Frameworkmentioning

confidence: 99%