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A Richly Annotated Pedestrian Dataset for Person Retrieval in Real Surveillance Scenarios

机译:真实监视场景中用于人员检索的带注释的行人数据集

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Retrieving specific persons with various types of queries, e.g., a set of attributes or a portrait photo has great application potential in large-scale intelligent surveillance systems. In this paper, we propose a richly annotated pedestrian (RAP) dataset which serves as a unified benchmark for both attribute-based and image-based person retrieval in real surveillance scenarios. Typically, previous datasets have three improvable aspects, including limited data scale and annotation types, heterogeneous data source, and controlled scenarios. Differently, RAP is a large-scale dataset which contains 84928 images with 72 types of attributes and additional tags of viewpoint, occlusion, body parts, and 2589 person identities. It is collected in the real uncontrolled scene and has complex visual variations in pedestrian samples due to the change of viewpoints, pedestrian postures, and cloth appearance. Towards a high-quality person retrieval benchmark, an amount of state-of-the-art algorithms on pedestrian attribute recognition and person re-identification (ReID), are performed for quantitative analysis with three evaluation tasks, i.e., attribute recognition, attribute-based and image-based person retrieval, where a new instance-based metric is proposed to measure the dependency of the prediction of multiple attributes. Finally, some interesting problems, e.g., the joint feature learning of attribute recognition and ReID, and the problem of cross-day person ReID, are explored to show the challenges and future directions in person retrieval.
机译:通过各种属性,例如一组属性或肖像照片来检索特定人员在大型智能监视系统中具有巨大的应用潜力。在本文中,我们提出了一个注释丰富的行人(RAP)数据集,该数据集可作为实际监视场景中基于属性和基于图像的人员检索的统一基准。通常,以前的数据集具有三个无法改进的方面,包括有限的数​​据规模和注释类型,异构数据源以及受控方案。不同的是,RAP是一个大规模数据集,其中包含84928幅图像,这些图像具有72种属性以及视点,遮挡,身体部位和2589个人身份的附加标签。它收集在真实的不受控制的场景中,由于视点,行人姿势和衣服外观的变化,行人样本的视觉变化也很复杂。为了实现高质量的人员检索基准,针对行人属性识别和人员重新识别(ReID)的最新技术进行了定量分析,并通过三个评估任务进行了评估,即属性识别,属性识别,基于和基于图像的人员检索,其中提出了一种新的基于实例的度量来测量多个属性预测的依赖性。最后,探讨了一些有趣的问题,例如属性识别和ReID的联合特征学习以及跨日人员ReID的问题,以显示人员检索中的挑战和未来方向。

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