Performance Measures and a Data Set for Multi-target, Multi-camera Tracking

被引：2116

作者：

Ristani, Ergys ^{[1
]}

Solera, Francesco ^{[2
]}

Zou, Roger ^{[1
]}

Cucchiara, Rita ^{[2
]}

Tomasi, Carlo ^{[1
]}

机构：

[1] Duke Univ, Dept Comp Sci, Durham, NC 27706 USA

[2] Univ Modena & Reggio Emilia, Dept Engn, Modena, Italy

来源：

COMPUTER VISION - ECCV 2016 WORKSHOPS, PT II | 2016年 / 9914卷

基金：

美国国家科学基金会;

关键词：

Performance evaluation; Multi camera tracking; Identity management; Multi camera data set; Large scale data set; TARGET TRACKING;

D O I：

10.1007/978-3-319-48881-3_2

中图分类号：

TP18 [人工智能理论];

学科分类号：

081104 ; 0812 ; 0835 ; 1405 ;

摘要：

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.

引用

页码：17 / 35

页数：19

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