Survey of amplitude‐aided multi‐target tracking methods

Bae, Seung-Hwan

doi:10.1049/iet-rsn.2018.5064

Cited by 7 publications

(3 citation statements)

References 32 publications

(67 reference statements)

Supporting

Mentioning

Contrasting

Order By: Relevance

“…We assume the probability density of an amplitude a follows a Rayleigh distribution as discussed in [39]. We then define the expected (or mean) SNR 2 d = S/N 0 , where S is the signal power and d can be treated as the expected object signal power because N 0 = 1.…”

Section: Amplitude Model and Unknown Snr Estimation 1) Object Amplmentioning

confidence: 99%

See 1 more Smart Citation

Online Multi-Object Tracking With Visual and Radar Features

Bae

2020

IEEE Access

Self Cite

View full text Add to dashboard Cite

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.

show abstract

Section: Amplitude Model and Unknown Snr Estimation 1) Object Amplmentioning

confidence: 99%

“…In this case, the signal returned from the object is expressed as the sum of the transmitted signal and the narrow band noise. The background noise is normalized as in [39]. This means that the expected noise power N 0 is unity.…”

Section: Amplitude Model and Unknown Snr Estimation 1) Object Amplmentioning

confidence: 99%

Online Multi-Object Tracking With Visual and Radar Features

Bae

2020

IEEE Access

Self Cite

View full text Add to dashboard Cite

show abstract

“…Hence, one has to take into account the fluctuation of the target SNR when amplitude information is used. In [17], [18], the uncertainty of the target SNR is modeled by random walk (RW) with Gaussian step size and the SNR is estimated by sequential Monte Carlo (SMC) method. Ristic et al [19] employ the Bernoulli filter with amplitude measurement for single target tracking, where the target SNR is also described by a RW process.…”

Section: Introductionmentioning

confidence: 99%