Dynamic environments are challenging for visual SLAM since the moving objects occlude the static environment features and lead to wrong camera motion estimation. In this paper, we present a novel dense RGB-D SLAM solution that simultaneously accomplishes the dynamic/static segmentation and camera ego-motion estimation as well as the static background reconstructions. Our novelty is using optical flow residuals to highlight the dynamic semantics in the RGB-D point clouds and provide more accurate and efficient dynamic/static segmentation for camera tracking and background reconstruction. The dense reconstruction results on public datasets and real dynamic scenes indicate that the proposed approach achieved accurate and efficient performances in both dynamic and static environments compared to state-of-the-art approaches.

This paper discusses the application of video motion capturing technology (VMocap) to a competitive team sports game. The setting introduces a specific set of constraints: large scale markerless motion capturing, big recording volume, transmitting and processing gigabytes of data, operation without interfering with players or distracting spectators and staff, etc... In this paper, we present how we tackled and successfully solved all of these constraints. That enabled us to analyze the sportsmen without any intrusions, while giving their peak performance, hence opening a new field for Mocap application. International volleyball game was recorded in full length with the described system. During the course of the event, we compressed 54TB of raw image data real-time, capturing 6 hours of high framerate video per camera, without disturbing any of the game operations. Using the data, we were able to reconstruct the motion, muscle activity and behavior of the athletes present on the court.

Summary Visual interaction is one of the most important ways for mobile robots to serve the elderly in the future. Mobile robots need to recognize human movements to follow human movements or evade actions. An indoor mobile robot interaction system based on cloud servers is presented. In a human‐machine coexistence environment, the mobile robot recognizes human motion, which represents the control intention for robot. In addition, if the person does not issue a control command, the robot follows the operator moving trajectory as a follower. A mobile robot with four Mecanum wheels is used in experiments. In the robot, a 3D camera is used to capture human motion and recognizes the language control commands. Then, the mobile robot executes the command action after translating the human body language to its motion direction control mode. Moreover, a 2D camera in the robot is used to autonomously capture 2D human multi‐view images and combines facial recognition to achieve specific human target to follow‐up. Some experiments are presented to verify the effectiveness of the whole Interaction control system.