REVIEW 2 cited by
A Solution for Large-scale Multi-object Tracking
Not yet reviewed by Pith; the record is open.
This paper has not been read by Pith yet. Machine review is queued; the pith claim, tier, and objections will appear here once it completes.
SPECIMEN: schema-true, not a live event
T0 review · schema-true
One-sentence machine reading of the paper's core claim.
pith:XXXXXXXX · record.json · timestamp
Signed reviews
read the original abstract
A large-scale multi-object tracker based on the generalised labeled multi-Bernoulli (GLMB) filter is proposed. The algorithm is capable of tracking a very large, unknown and time-varying number of objects simultaneously, in the presence of a high number of false alarms, as well as misdetections and measurement origin uncertainty due to closely spaced objects. The algorithm is demonstrated on a simulated large-scale tracking scenario, where the peak number objects appearing simultaneously exceeds one million. To evaluate the performance of the proposed tracker, we also introduce a new method of applying the optimal sub-pattern assignment (OSPA) metric, and an efficient strategy for its evaluation in large-scale scenarios.
Forward citations
Cited by 2 Pith papers
-
A Merge/Split Algorithm for Multitarget Tracking Using Generalized Labeled Multi-Bernoulli Filters
A Merge/Split algorithm uses measurement-ID indexing and a tolerance-based independence test to adaptively factorize dGLMB filtering densities into independently updated factors.
-
Aerial multi-object tracking by detection using deep association networks
A RetinaNet detector with six anchor scales and SE blocks, combined with a COCO-trained DeepSORT association network, is evaluated for detection and tracking on VisDrone aerial data.
Discussion (0). Continue with ORCID to comment.