With the major adoption of automation for cities security, person re-identification (Re-ID) has been extensively studied recently. In this paper, we argue that the current way of studying person re-identification, i.e. by trying to re-identify a person within already detected and pre-cropped images of people, is not sufficient to implement practical security applications, where the inputs to the system are the full frames of the video streams. To support this claim, we introduce the Full Frame Person Re-ID setting (FF-PRID) and define specific metrics to evaluate FF-PRID implementations. To improve robustness, we also formalize the hybrid human-machine collaboration framework, which is inherent to any Re-ID security applications. To demonstrate the importance of considering the FF-PRID setting, we build an experiment showing that combining a good people detection network with a good Re-ID model does not necessarily produce good results for the final application. This underlines a failure of the current formulation in assessing the quality of a Re-ID model and justifies the use of different metrics. We hope that this work will motivate the research community to consider the full problem in order to develop algorithms that are better suited to real-world scenarios.

随着自动化技术在城市安全中的广泛采用，最近对人的重新识别（Re-ID）进行了广泛的研究。在本文中，我们认为，目前研究人员重新识别的方法（即通过尝试在已检测到并预先裁剪的人员图像中重新识别人员）不足以实现实际的安全应用，系统是视频流的完整帧。为了支持此主张，我们引入了“全帧人员重新ID”设置（FF-PRID），并定义了特定的指标来评估FF-PRID的实现。为了提高鲁棒性，我们还对任何Re-ID安全应用程序固有的混合人机协作框架进行了形式化。为了说明考虑FF-PRID设置的重要性，我们建立了一个实验，表明将良好的人员检测网络与良好的Re-ID模型相结合并不一定会对最终应用产生良好的结果。这突出了当前公式在评估Re-ID模型的质量方面的失败，并证明了使用不同指标的合理性。我们希望这项工作能够激励研究界考虑整个问题，以便开发出更适合现实情况的算法。