PRISM：通过结构化匹配重新识别人员
PRISM: Person Re-Identification via Structured Matching
摘要：
人的重新识别（re-id）是视觉监控中的一个新兴问题，它处理的是当个人穿越摄像机网络监控的各个位置时，如何维护个人的实体。从视觉的角度来看，re-id是具有挑战性的，因为在具有不同姿态、照明和校准的相机中，个体的视觉外观会发生显著变化。在全球范围内，挑战来自于需要在不同相机视图中的所有个体实体之间保持结构一致的匹配。我们提出了PRISM，一个结构化的匹配方法来共同应对这些挑战。我们将全局问题视为一个加权图匹配问题，并通过学习来估计边缘权重，以基于训练样本中视觉模式的同现来预测它们。这些基于同现的分数反过来通过推断训练实例中出现的可能和不可能的视觉同现来解释外观变化。我们在Single Shot和Multi-Shot场景中实现PRISM。PRISM在匹配率方面一致优于最先进的技术，同时计算效率高。
英文摘要：
Person re-identification (re-id), an emerging problem in visual surveillance, deals with maintaining entities of individuals whilst they traverse various locations surveilled by a camera network. From a visual perspective re-id is challenging due to significant changes in visual appearance of individuals in cameras with different pose, illumination and calibration. Globally the challenge arises from the need to maintain structurally consistent matches among all the individual entities across different camera views. We propose PRISM, a structured matching method to jointly account for these challenges. We view the global problem as a weighted graph matching problem and estimate edge weights by learning to predict them based on the co-occurrences of visual patterns in the training examples. These co-occurrence based scores in turn account for appearance changes by inferring likely and unlikely visual co-occurrences appearing in training instances. We implement PRISM on single shot and multi-shot scenarios. PRISM uniformly outperforms state-of-the-art in terms of matching rate while being computationally efficient.
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
