PERIAPT: Joint Person Detection, Re-Identification and Pose Tracking in Video

PERIAPT：视频中的联合人员检测、重新识别和姿势跟踪

• 批准号: 410904267
• 负责人: Professor Dr. Jürgen Gall
• 金额: --
• 依托单位: Institut für Informatik
• 依托单位国家: 德国
• 项目类别: Research Grants
• 财政年份: 2019
• 资助国家: 德国
• 起止时间: 2018-12-31 至 2022-12-31
• 项目状态: 已结题
• 来源: https://gepris.dfg.de/gepris/projekt/410904267?language=en
• 关键词: PERIAPT; Joint; Person; Detection; Re

For many applications like robotics, autonomous driving, or smart factories, machines have to be aware of humans in their vicinity. This requires to know the location and pose of the present humans and track them continuously. Although person detection, person reidentification and pose tracking are highly correlated, the three tasks have been previously studied independently. For instance, the alignment quality of the bounding boxes of detected persons affects the re-identification accuracy. If a person is occluded for a long time or re-appears after leaving the scene, it needs to be re-identified. Furthermore, person detectors struggle to detect partially occluded persons whereas visible joints can be detected even if other parts of the human body are occluded. We will therefore investigate how the three tasks can assist each other and we aim to improve the accuracy for all three tasks by solving them together in a joint framework.

对于机器人、自动驾驶或智能工厂等许多应用，机器必须意识到附近的人类。这需要知道当前人类的位置和姿势，并持续跟踪他们。虽然人的检测，人的重新识别和姿态跟踪是高度相关的，这三个任务以前已经独立研究。例如，检测到的人的边界框的对准质量影响重新识别精度。如果一个人长时间被遮挡，或者离开现场后重新出现，就需要重新识别。此外，人检测器难以检测部分被遮挡的人，而即使人体的其他部分被遮挡，也可以检测可见的关节。因此，我们将研究这三个任务如何相互帮助，我们的目标是通过在一个联合框架中一起解决它们来提高所有三个任务的准确性。