Transfer Learning for Person Re-identification

身份识别是分布式多摄像机监控中的一项重要任务。目前，这是以巨大的经济成本手动执行的，并且由于操作员的注意力差距而具有高错误率。在这个项目中，我们的目标是实现快速、准确和健壮的自动人员重新识别，可以部署到任何给定的摄像机网络场景中，而不需要任何昂贵的校准步骤。自动人员重新识别是指根据视频中捕获的图像在不同时间跨不同空间分布的摄像机视图关联人员的任务。这是具有挑战性的，因为人体的清晰度和各种观察条件，如光线、角度和距离，意味着同一人在不同的视角下观察到的外观通常比不同人的差异更大。同时，这是一个需要解决的重要任务，因为重新识别是视觉监控中许多关键能力的基础，例如多摄像机跟踪。对于需要视频分析以实现零售优化、运营效率、公共安全、安保、基础设施保护和恐怖主义预防等各种目标的最终用户组织来说，这又是一项关键功能。此外，自动重新识别是很重要的，因为在大型摄像机网络中，人工过程不仅昂贵得令人望而却步，而且由于注意力的差距而不准确。当前最先进的重新识别系统使用机器学习技术来产生模型，用于基于对特定摄像机中的个人身份的手动注释来重新识别这些摄像机。然而，这在实践中是不可扩展的，因为每一对独特的相机都需要使用训练数据进行校准。在这个项目中，我们将开发新的机器学习模型，该模型可以自动调整为初始源摄像机集创建的重新识别模型，以解决每一对新摄像机的重新识别问题，而不需要新的注释。这将大大提高重新识别技术的实际影响，使其更加准确，而且更便宜和更容易部署。

Person re-identification is an important task in distributed multi-camera surveillance. This is currently performed manually at great economic cost, and with high error rates due to operator attentive gaps. In this project we aim to achieve fast accurate and robust automated person re-identification that can be deployed to any given camera network scenario, without any expensive calibration steps.Automated person re-identification is the task of associating people based on images captured in video across diverse spatially distributed camera views at different times. This is challenging because the articulation of the human body and variety of viewing conditions such as lighting, angle and distance means that observed appearance typically differs more for the same person in different views than it does for different people. At the same time, it is an important task to solve because re-identification underpins many key capabilities in visual surveillance such as multi-camera tracking. This in turn is a key capability for end-user organizations which need video analytics to achieve a variety of ends including retail optimization, operational efficiency, public safety, security, infrastructure protection and terrorism prevention. Moreover, it is important to automate re-identification because the manual process in large camera networks is both prohibitively costly and inaccurate due to attentive gaps.Current state of the art re-identification systems use machine learning techniques to produce models for re- identifying across a particular pair of cameras based on manual annotation of person identity in those cameras. However, this is not scalable in practice, because every unique pair of cameras would need calibration with training data. In this project, we will develop new machine learning models that can automatically adapt re-identification models created for an initial set of source cameras to address the re-identification problem in each new pair of cameras without requiring new annotation. This will dramatically improve the practical impact of re-identification technology by making it significantly more accurate as well as cheaper and easier to deploy.