Markerless outside-in tracking: Difference between revisions

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'''[[Markerless outside-in tracking]]''' is a subtype of [[positional tracking]] used in [[virtual reality]] (VR) and [[augmented reality]] (AR). In this approach, external [[camera]]s or other [[depth sensing]] devices positioned in the environment estimate the six-degree-of-freedom pose of a user or object without relying on any [[fiducial marker]]s. Instead, [[computer vision]] algorithms analyse the incoming colour or depth stream to detect and follow natural scene features or the user’s own body, enabling real-time [[motion capture]] and interaction.<ref name="Shotton2011" />

* **Sensing layer** – One or more fixed [[RGB-D]] or [[infrared]] depth cameras acquire per-frame point clouds. Commodity devices such as the Microsoft Kinect project a [[structured light]] pattern or use [[time-of-flight]] methods to compute depth maps.<ref name="Zhang2012" />

* **Segmentation** – Foreground extraction or person segmentation isolates user pixels from the static background.

* **Per-pixel body-part classification** – A machine-learning model labels each pixel as “head”, “hand”, “torso”, and so on (e.g., the Randomised Decision Forest used in the original Kinect).<ref name="Shotton2011" />

* **Skeletal reconstruction and filtering** – The system fits a kinematic skeleton to the classified pixels and applies temporal filtering to reduce jitter, producing smooth head- and hand-pose data that can drive VR/AR applications.

Although a single camera can suffice, multi-camera rigs extend coverage and mitigate occlusion problems. Open source and proprietary middleware (e.g., [[OpenNI]]/NITE, the Microsoft Kinect SDK) expose joint-stream APIs for developers.<ref name="OpenNI2013" />

Marker-based outside-in systems (HTC Vive Lighthouse, PlayStation VR) attach active LEDs or retro-reflective spheres to the headset or controllers; external sensors triangulate these explicit targets, achieving sub-millimetre precision and sub-10 ms latency. Markerless alternatives dispense with physical targets, improving user comfort and reducing setup time, but at the cost of:

* **Lower positional accuracy and higher latency** – Depth-sensor noise and computational overhead introduce millimetre- to centimetre-level error and ~20–30 ms end-to-end latency.<ref name="Baker2016" />

* **Sensitivity to occlusion** – If a body part leaves the camera’s line of sight, the model loses track until the part re-enters view.

| 2003 || [[EyeToy]] (PlayStation 2) || 2-D silhouette tracking with a single RGB camera for casual gesture-based games.<ref name="Sony2003" />

| 2014 – 2016 || Research prototypes || Studies showed Kinect V2 could supply 6-DOF head, hand, and body input to DIY VR HMDs.<ref name="KinectVRStudy" />

* **Gaming and Entertainment** – Titles like ''Kinect Sports'' mapped whole-body actions directly onto avatars. Enthusiast VR chat platforms still use Kinect skeletons to animate full-body avatars.

* **Rehabilitation and Exercise** – Clinicians employ depth-based pose tracking to monitor range-of-motion exercises without encumbering patients with sensors.<ref name="Baker2016" />

* **Interactive installations** – Museums deploy wall-mounted depth cameras to create “magic-mirror” AR exhibits that overlay virtual costumes onto visitors in real time.

* **Telepresence** – Multi-Kinect arrays stream volumetric representations of remote participants into shared virtual spaces.

* '''Reduced accuracy and jitter''' – Compared with marker-based solutions, joint estimates exhibit higher positional noise, especially during fast or complex motion.<ref name="Baker2016" />

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