Person and vehicle re-identification (re-ID) are important challenges for the analysis of the burgeoning collection of urban surveillance videos. To efficiently evaluate such videos, which are populated with both vehicles and pedestrians, it would be preferable to have one unified framework with effective performance across both domains. Unfortunately, due to the contrasting composition of humans and vehicles, no architecture has yet been established that can adequately perform both tasks. We release a Person and Vehicle Unified Data Set (PVUD) comprising of both pedestrians and vehicles from popular existing re-ID data sets, in order to better model the data that we would expect to find in the real world. We exploit the generalisation ability of metric learning to propose a re-ID framework that can learn to re-identify humans and vehicles simultaneously. We design our network, MidTriNet, to harness the power of mid-level features to develop better representations for the re-ID tasks. We help the system to handle mixed data by appending unification terms with additional hard negative and hard positive mining to MidTriNet. We attain comparable accuracy training on PVUD to training on the comprising data sets separately, supporting the system's generalisation power. To further demonstrate the effectiveness of our framework, we also obtain results better than, or competitive with, the state-of-the-art on each of the Market-1501, CUHK03, VehicleID and VeRi data sets.

中文翻译：

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统一人员和车辆重新识别


人员和车辆重新识别 (re-ID) 是分析不断增长的城市监控视频的重要挑战。为了有效地评估此类充满车辆和行人的视频，最好有一个跨两个领域都具有有效性能的统一框架。不幸的是，由于人类和车辆的构成截然不同，目前还没有建立能够充分执行这两项任务的架构。我们发布了人车统一数据集 (PVUD)，其中包含来自流行的现有重识别数据集的行人和车辆，以便更好地对我们期望在现实世界中找到的数据进行建模。我们利用度量学习的泛化能力提出了一个重新识别框架，可以学习同时重新识别人和车辆。我们设计的网络 MidTriNet 是为了利用中级特征的力量来为 re-ID 任务开发更好的表示。我们通过向 MidTriNet 附加统一项以及额外的硬负和硬正挖掘来帮助系统处理混合数据。我们在 PVUD 上获得的训练精度与在单独包含的数据集上进行的训练相当，支持系统的泛化能力。为了进一步证明我们框架的有效性，我们还在 Market-1501、CUHK03、VehicleID 和 VeRi 数据集上获得了优于或具有竞争力的最新结果。