Abstract: A system and method for human motion detection and tracking are disclosed. In one embodiment, a smart device having an optical sensing instrument monitors a stage. Memory is accessible to a processor and communicatively coupled to the optical sensing instrument. The system captures an image frame from the optical sensing instrument. The image frame is then converted into a designated image frame format, which is provided to a pose estimator. A two-dimensional dataset is received from the pose estimator. The system then converts, using inverse kinematics, the two-dimensional dataset into a three-dimensional dataset, which includes time-independent static joint positions, and then calculates, using the three-dimensional dataset, the position of each of the respective plurality of body parts in the image frame.

Abstract: A system and method for human motion detection and tracking are disclosed. In one embodiment, an optical sensing instrument monitors a stage. A memory is accessible to a processor and communicatively coupled to the optical sensing instrument. The system captures a depth frame from the optical sensing instrument. The depth frame may include at each image element first coordinate values including a point related to a distance from the optical sensing instrument. The depth frame is converted into a designated depth frame format, which includes at each image element second coordinate values relative to the depth frame. Probability distribution models are applied to the designated depth frame format to identify a respective plurality of body parts. The position of each of the respective plurality of body parts in the designated depth frame is calculated as is the position of each of the plurality of body parts in the depth frame.

Abstract: A system and method for human motion detection and tracking are disclosed. In one embodiment, a smart device having an optical sensing instrument monitors a stage. Memory is accessible to a processor and communicatively coupled to the optical sensing instrument. The system captures an image frame from the optical sensing instrument. The image frame is then converted into a designated image frame format, which is provided to a pose estimator. A two-dimensional dataset is received from the pose estimator. The system then converts, using inverse kinematics, the two-dimensional dataset into a three-dimensional dataset, which includes time-independent static joint positions, and then calculates, using the three-dimensional dataset, the position of each of the respective plurality of body parts in the image frame.

Abstract: A system and method for human motion detection and tracking are disclosed. In one embodiment, an optical sensing instrument monitors a stage. A memory is accessible to a processor and communicatively coupled to the optical sensing instrument. The system captures a depth frame from the optical sensing instrument. The depth frame may include at each image element first coordinate values including a point related to a distance from the optical sensing instrument. The depth frame is converted into a designated depth frame format, which includes at each image element second coordinate values relative to the depth frame. Probability distribution models are applied to the designated depth frame format to identify a respective plurality of body parts. The position of each of the respective plurality of body parts in the designated depth frame is calculated as is the position of each of the plurality of body parts in the depth frame.

Abstract: A system and method for human motion detection and tracking are disclosed. In one embodiment, a smart device having an optical sensing instrument monitors a stage having a mat. Memory is accessible to a processor and communicatively coupled to the optical sensing instrument. The system captures an image frame from the optical sensing instrument while capturing a data frame from the mat. The image frame is then converted into a designated image frame format, which is provided to a pose estimator. A two-dimensional dataset is received from the pose estimator. The system then converts, using inverse kinematics, the two-dimensional dataset into a three-dimensional dataset, which includes time-independent static joint positions, and then calculates, using the three-dimensional dataset in conjunction with the data frame, the position of each of the respective plurality of body parts in the image frame.

Abstract: A system and method for human motion detection and tracking are disclosed. In one embodiment, a smart device having an optical sensing instrument monitors a stage. Memory is accessible to a processor and communicatively coupled to the optical sensing instrument. The system captures an image frame from the optical sensing instrument. The image frame is then converted into a designated image frame format, which is provided to a pose estimator. A two-dimensional dataset is received from the pose estimator. The system then converts, using inverse kinematics, the two-dimensional dataset into a three-dimensional dataset, which includes time-independent static joint positions, and then calculates, using the three-dimensional dataset, the position of each of the respective plurality of body parts in the image frame.

Abstract: A system and method for human motion detection and tracking (10) are disclosed. In one embodiment, an optical sensing instrument (16) monitors a stage (24). A memory (182) is accessible to a processor (180) and communicatively coupled to the optical sensing instrument (16). The system (10) captures a depth frame (150) from the optical sensing instrument (16). The depth frame (150) is converted into a designated depth frame format (152), which includes at each image element second coordinate values relative to the depth frame (150). Probability distribution models are applied to the designated depth frame format (152) to identify a respective plurality of body parts. The position of each of the respective plurality of body parts in the designated depth frame format (152) is calculated as is the position of each of the respective plurality of body parts in the depth frame (150).