Performance Measures and a Data Set for Multi-Target, Multi-Camera Tracking
Ergys Ristani, Francesco Solera, Roger S. Zou, Rita Cucchiara, Carlo Tomasi
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- github.com/JonathonLuiten/TrackEvalnone★ 1,205
- github.com/JonathonLuiten/HOTA-metricsnone★ 1,205
- github.com/syliz517/clip-reidpytorch★ 465
- github.com/yxgeee/SpCLpytorch★ 332
- github.com/zkcys001/UDAStrongBaselinepytorch★ 144
- github.com/visipedia/caltech-fish-countingnone★ 54
- github.com/chenhao2345/GCLpytorch★ 48
- github.com/chenhao2345/ICEpytorch★ 48
- github.com/adhirajghosh/rptm_reidpytorch★ 35
- github.com/WangWenhao0716/Attentive-WaveBlockpytorch★ 30
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.