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

• Applicant: Professor Dr. Jürgen Gall
• Subject Area: Image and Language Processing, Computer Graphics and Visualisation, Human Computer Interaction, Ubiquitous and Wearable Computing
• Term: from 2019 to 2023
• Project identifier: Deutsche Forschungsgemeinschaft (DFG) - Project number 410904267

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.

• DFG Programme: Research Grants
• International Connection: China
• Partner Organisation: National Natural Science Foundation of China
• Cooperation Partner: Professorin Dr. Shanshan Zhang