Scalable Person Re-Identification
Scalable Person Re-identification: A Benchmark Liang Zheng†‡∗, Liyue Shen†∗, Lu Tian†∗, Shengjin Wang†, Jingdong Wang§, Qi Tian‡ †Tsinghua University §Microsoft Research ‡University of Texas at San Antonio Abstract eras; each identity has one image under each camera, so the number of queries and relevant images is very limited. Fur- This paper contributes a new high quality dataset for thermore, in most datasets, pedestrians are well-aligned by person re-identification, named “Market-1501”. General- hand-drawn bboxes (bboxes). But in reality, when pedes- ly, current datasets: 1) are limited in scale; 2) consist of trian detectors are used, the detected persons may undergo hand-drawn bboxes, which are unavailable under realistic misalignment or part missing (Fig. 1). On the other hand, settings; 3) have only one ground truth and one query im- pedestrian detectors, while producing true positive bbox- age for each identity (close environment). To tackle these es, also yield false alarms caused by complex background problems, the proposed Market-1501 dataset is featured in or occlusion (Fig. 1). These distractors may exert non- three aspects. First, it contains over 32,000 annotated b- ignorable influence on recognition accuracy. As a result, boxes, plus a distractor set of over 500K images, making current methods may be biased toward ideal settings and it the largest person re-id dataset to date. Second, im- their effectiveness may be impaired once the ideal dataset ages in Market-1501 dataset are produced using the De- meets reality. To address this problem, it is important to formable Part Model (DPM) as pedestrian detector.
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