论文标题
集团：时空对象在城市尺度上重新识别
Clique: Spatiotemporal Object Re-identification at the City Scale
论文作者
论文摘要
物体重新识别（REID）是城市规模相机的关键应用。尽管经典的REID任务通常被视为图像检索，但我们将其视为目标对象出现的位置和时间的时空查询。时空的REID受到计算机视觉算法的准确性限制和城市摄像机的巨大视频的挑战。我们提出了一个实用的REID引擎集团，它以两种新技术为基础：（1）Clique通过将REID算法提取的模糊对象特征群群来评估目标发生，每个集群代表要与输入相匹配的独特对象的一般印象； （2）在搜索视频中，集团样品摄像机以最大化时空覆盖范围，并逐步添加用于按需处理的摄像机。通过评估25个相机的25个小时的视频，集团在70个查询中达到了0.87的高度精度（召回5），并以830倍的视频实时运行，以实现高精度。
Object re-identification (ReID) is a key application of city-scale cameras. While classic ReID tasks are often considered as image retrieval, we treat them as spatiotemporal queries for locations and times in which the target object appeared. Spatiotemporal reID is challenged by the accuracy limitation in computer vision algorithms and the colossal videos from city cameras. We present Clique, a practical ReID engine that builds upon two new techniques: (1) Clique assesses target occurrences by clustering fuzzy object features extracted by ReID algorithms, with each cluster representing the general impression of a distinct object to be matched against the input; (2) to search in videos, Clique samples cameras to maximize the spatiotemporal coverage and incrementally adds cameras for processing on demand. Through evaluation on 25 hours of videos from 25 cameras, Clique reached a high accuracy of 0.87 (recall at 5) across 70 queries and runs at 830x of video realtime in achieving high accuracy.
