Pith. sign in

REVIEW 4 cited by

Simple Online and Realtime Tracking

Not yet reviewed by Pith; the record is open.

Re-run · record.json Download PDF Read on arXiv ↗

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

arxiv 1602.00763 v2 pith:CGQ3R55E submitted 2016-02-02 cs.CV

Alex Bewley , Zongyuan Ge , Lionel Ott , Fabio Ramos , Ben Upcroft This is my paper · ORCID
classification cs.CV
keywords trackingonlineapproachrealtimestate-of-the-arttrackersaccuracyachieves
verification ladder T0 review T1 audit T2 compute T3 formal
0 comments
read the original abstract

This paper explores a pragmatic approach to multiple object tracking where the main focus is to associate objects efficiently for online and realtime applications. To this end, detection quality is identified as a key factor influencing tracking performance, where changing the detector can improve tracking by up to 18.9%. Despite only using a rudimentary combination of familiar techniques such as the Kalman Filter and Hungarian algorithm for the tracking components, this approach achieves an accuracy comparable to state-of-the-art online trackers. Furthermore, due to the simplicity of our tracking method, the tracker updates at a rate of 260 Hz which is over 20x faster than other state-of-the-art trackers.

Discussion (0). Continue with ORCID to comment.

Forward citations

Cited by 4 Pith papers

Reviewed papers in the Pith corpus that reference this work. Sorted by Pith novelty score. Full citation record

  1. Tree-SLAM: semantic object SLAM for efficient mapping of individual trees in orchards

    cs.RO 2025-07 conditional novelty 6.0 of 10

    A factor-graph SLAM that uses YOLOv8 trunk detections and a cascade-graph association algorithm maps individual orchard trees with a best-case geo-localization error of 18 cm, beating a clustering baseline on most test rows.

  2. Deep Learning for Accurate Vision-based Catch Composition in Tropical Tuna Purse Seiners

    cs.CV 2025-11 conditional novelty 5.0 of 10

    YOLOv9+SAM2 segmentation with hierarchical classification estimates tuna catch composition from EM video with about 4.5% mean absolute error on controlled test operations.

  3. A Real-Time, Vision-Based System for Badminton Smash Speed Estimation on Mobile Devices

    cs.CV 2025-09 reject novelty 3.0 of 10

    A phone-based YOLOv5+Kalman pipeline estimates badminton smash speed at impact, reporting much higher values than a radar gun, but the validation and released artifacts are insufficient.

  4. Feedback Driven Multi Stereo Vision System for Real-Time Event Analysis

    cs.CV 2025-06 conditional novelty 3.0 of 10

    A proposed multi-camera stereo pipeline for real-time event analysis is described, but only preliminary single-camera accuracy and FPS tests are shown, no code or data is released, and the core fusion and feedback com...

Pith tools