Privacy preserving dynamic room layout mapping

© Springer International Publishing Switzerland 2016. We present a novel and efficient room layout mapping strategy that does not reveal people’s identity. The system uses only a Kinect depth sensor instead of RGB cameras or a high-resolution depth sensor. The users’ facial details will neither be captured nor recognized by the system. The system recognizes and localizes 3D objects in an indoor environment, that includes the furniture and equipment, and generates a 2D map of room layout. Our system accomplishes layout mapping in three steps. First, it converts a depth image from the Kinect into a top-view image. Second, our system processes the top-view image by restoring the missing information from occlusion caused by moving people and random noise from Kinect depth sensor. Third, it recognizes and localizes different objects based on their shape and height for a given top-view image. We evaluated this system in two challenging real-world application scenarios: a laboratory room with four people present and a trauma room with up to 10 people during actual trauma resuscitations. The system achieved 80 % object recognition accuracy with 9.25 cm average layout mapping error for the laboratory furniture scenario and 82 % object recognition accuracy for the trauma resuscitation scenario during six actual trauma cases

Privacy preserving dynamic room layout mapping

© Springer International Publishing Switzerland 2016. We present a novel and efficient room layout mapping strategy that does not reveal people’s identity. The system uses only a Kinect depth sensor instead of RGB cameras or a high-resolution depth sensor. The users’ facial details will neither be captured nor recognized by the system. The system recognizes and localizes 3D objects in an indoor environment, that includes the furniture and equipment, and generates a 2D map of room layout. Our system accomplishes layout mapping in three steps. First, it converts a depth image from the Kinect into a top-view image. Second, our system processes the top-view image by restoring the missing information from occlusion caused by moving people and random noise from Kinect depth sensor. Third, it recognizes and localizes different objects based on their shape and height for a given top-view image. We evaluated this system in two challenging real-world application scenarios: a laboratory room with four people present and a trauma room with up to 10 people during actual trauma resuscitations. The system achieved 80 % object recognition accuracy with 9.25 cm average layout mapping error for the laboratory furniture scenario and 82 % object recognition accuracy for the trauma resuscitation scenario during six actual trauma cases