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Online Multi-Object Tracking

The goal of Online Multi-Object Tracking is to estimate the spatio-temporal trajectories of multiple objects in an online video stream (i.e., the video is provided frame-by-frame), which is a fundamental problem for numerous real-time applications, such as video surveillance, autonomous driving, and robot navigation.

Source: A Hybrid Data Association Framework for Robust Online Multi-Object Tracking

Papers

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Showing 1–10 of 56 papers

TitleStatusHype
PP-YOLOE: An evolved version of YOLOCode4
Focusing on Tracks for Online Multi-Object TrackingCode2
CAMELTrack: Context-Aware Multi-cue ExpLoitation for Online Multi-Object TrackingCode2
SFSORT: Scene Features-based Simple Online Real-Time TrackerCode2
Hybrid-SORT: Weak Cues Matter for Online Multi-Object TrackingCode2
Large-Scale Pre-training for Person Re-identification with Noisy LabelsCode2
Lost and Found: Overcoming Detector Failures in Online Multi-Object TrackingCode1
PuTR: A Pure Transformer for Decoupled and Online Multi-Object TrackingCode1
Detection Recovery in Online Multi-Object Tracking with Sparse Graph TrackerCode1
Real-time Online Multi-Object Tracking in Compressed DomainCode1
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