Body Tracking Overview Body tracking module focuses on person’s bones detection and tracking. A detected bone is represented by its two end points also called keypoints. The ZED camera is able to provide 2D and 3D information of each detected keypoints. Furthermore, it produces local rotation between neighbor bones. How It Works The overall process is very similar to the ZED SDK Object detection module. They share some information in outputs like the 3D position and 3D velocity of each person. Body tracking module also uses neural network for keypoints detection and then calls depth and positional tracking of the ZED SDK module to get the final 3D position of each keypoint. The ZED SDK supports two Body formats : BODY_FORMAT::POSE_18 are organized as follow : Each keypoint is indexed by an integer ranging from 0 to 17 : keypoint index keypoint name 0 NOSE 1 NECK 2 RIGHT_SHOULDER 3 RIGHT_ELBOW 4 RIGHT_WRIST 5 LEFT_SHOULDER 6 LEFT_ELBOW 7 LEFT_WRIST 8 RIGHT_HIP 9 RIGHT_KNEE 10 RIGHT_ANKLE 11 LEFT_HIP 12 LEFT_KNEE 13 LEFT_ANKLE 14 RIGHT_EYE 15 LEFT_EYE 16 RIGHT_EAR 17 LEFT_EAR BODY_FORMAT::POSE_34 are organized as follow : Each keypoint is indexed by integer from 0 to 33 : keypoint index keypoint name 0 PELVIS 1 NAVAL_SPINE 2 CHEST_SPINE 3 NECK 4 LEFT_CLAVICLE 5 LEFT_SHOULDER 6 LEFT_ELBOW 7 LEFT_WRIST 8 LEFT_HAND 9 LEFT_HANDTIP 10 LEFT_THUMB 11 RIGHT_CLAVICLE 12 RIGHT_SHOULDER 13 RIGHT_ELBOW 14 RIGHT_WRIST 15 RIGHT_HAND 16 RIGHT_HANDTIP 17 RIGHT_THUMB 18 LEFT_HIP 19 LEFT_KNEE 20 LEFT_ANKLE 21 LEFT_FOOT 22 RIGHT_HIP 23 RIGHT_KNEE 24 RIGHT_ANKLE 25 RIGHT_FOOT 26 HEAD 27 NOSE 28 LEFT_EYE 29 LEFT_EAR 30 RIGHT_EYE 31 RIGHT_EAR 32 LEFT_HEEL 33 RIGHT_HEEL The ZED SDK is able to output 3 levels of information : raw 2D/3D body detection, 3D body tracking and 3D body fitting. 2D/3D Body detection The ZED SDK first uses the ZED camera image to infer all 2D bones and keypoints using neural networks. Then the SDK depth module and positional tracking module are used together to extract the correct 3D position of each bones and keypoints. 3D body tracking If tracking is enabled, the ZED SDK will assign an identity to each detected body over time. At the same time, by filtering the raw body detection, it will output a more stable 3d body estimation. 3D body fitting Moreover, user can enable fitting to unlock even more information about each identity. The fitting process takes the history of each tracked person to deduce all missing keypoints thanks to the human kinematic’s constraint used by the body tracking module. It is also able to extract local rotation between a pair of neighbor bones by solving the inverse kinematic problem. These data will be compatible with some known software for avataring for example. Here is an example where BODY_FORMAT::POSE_34 were used to animate an avatar in Unreal. Detection Outputs Each detected person is stored as a structure in the ZED SDK and extends the same structure as in Object detection. See the Object Detection Outputs for Body tracking and Object detection shared attributes. The following new attributes are only filled by body tracking module. Object Data Description Output 2D keypoint A set of useful points representing the human body, expressed in 2D. a vector of [x,y] keypoint A set of useful points representing the human body, expressed in 3D. a vector of [x, y, z] 2D head bounding box bounds the head with four 2D points. Four pixel coordinates 3D head bounding box bounds the head with eight 3D points. Eight 3D coordinates head position 3D head centroid [x, y, z] keypoint confidence Per keypoint detection confidence a vector of float local position per joint stores local position of each keypoint a vector of [x,y,z] local orientation per joint stores local rotation of each keypoint a vector of [x,y,z,w] global root orientation stores the global root orientation of the Body [x,y,z,w] For more information on Body Tracking, see the Using the API page.