NOOUGAT unifies online and offline multi-object tracking with a GNN that processes non-overlapping subclips fused by an Autoregressive Long-term Tracking layer, reporting SOTA gains on DanceTrack, SportsMOT, and MOT20.
Performance Measures and a Data Set for Multi-Target, Multi-Camera Tracking
2 Pith papers cite this work. Polarity classification is still indexing.
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Pith papers citing it
abstract
To help accelerate progress in multi-target, multi-camera tracking systems, we present (i) a new pair of precision-recall measures of performance that treats errors of all types uniformly and emphasizes correct identification over sources of error; (ii) the largest fully-annotated and calibrated data set to date with more than 2 million frames of 1080p, 60fps video taken by 8 cameras observing more than 2,700 identities over 85 minutes; and (iii) a reference software system as a comparison baseline. We show that (i) our measures properly account for bottom-line identity match performance in the multi-camera setting; (ii) our data set poses realistic challenges to current trackers; and (iii) the performance of our system is comparable to the state of the art.
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cs.CV 2verdicts
UNVERDICTED 2representative citing papers
PH-GCN constructs a hierarchical graph of person parts and performs local/global feature learning via message passing in an end-to-end network for person re-identification.
citing papers explorer
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NOOUGAT: Towards Unified Online and Offline Multi-Object Tracking
NOOUGAT unifies online and offline multi-object tracking with a GNN that processes non-overlapping subclips fused by an Autoregressive Long-term Tracking layer, reporting SOTA gains on DanceTrack, SportsMOT, and MOT20.
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PH-GCN: Person Re-identification with Part-based Hierarchical Graph Convolutional Network
PH-GCN constructs a hierarchical graph of person parts and performs local/global feature learning via message passing in an end-to-end network for person re-identification.