Performance measures and a data set for multi-target, multi-camera tracking
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 1080 p, 60 fps video taken by 8 cameras observing more than 2, 700 identities over 85 min; 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.
Citation:Ristani, E.; Solera, F.; Zou, R.; Cucchiara, R.; Tomasi, C. "Performance measures and a data set for multi-target, multi-camera tracking" Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics), vol. 9914, nld, pp. 17 -35 , 2016, 2016 DOI: 10.1007/978-3-319-48881-3_2