Tracking Technology Comparison

Tracking Technology Comparison

OpenPTrack was created to support interactive projects by UCLA REMAP, our collaborators, students, and colleagues.

No one sensing technology is the best fit for every project. REMAP uses a variety of systems for sensing position, gesture, and pose in our work—from high end commercial motion capture to inexpensive open source solutions for blob tracking.

By comparing some popular approaches (that we also use), the table below illustrates the gap that OpenPTrack aims to fill: scalable, robust, real-time person tracking using affordable off-the-shelf components and an open source codebase.


OpenPTrack Single Kinect Blob Tracking
Augmented Reality Motion Capture
Motion Capture
RF Tracking
(w/ many popular frameworks) (e.g., Community Core Vision) (e.g., AR Toolkit) (e.g., Vicon) (e.g., Open Perception) (e.g., Zebra)
Target audience Education, Arts, Culture Various Various Various High-end Production High-end Production Industrial sensing
Core technology Networked 3D imagers 3D Imager 2D Camera(s) 2D Camera 2D Cameras 2D Cameras Radio Frequency
Output Type ID, 3D Centroid ID, Skeletal Data ID, 2D Centroid ID, 3D Position, Orientation ID, Dense Skeletal Data ID, Skeletal Data ID, 3D Position
Max. Tracking Volume Large Small Small to Medium Small to Medium Can be very large Medium Can be very large
Fusion of multiple views Intrinsic N/A Up to developer Up to developer Intrinsic Intrinsic N/A
Typical refresh rate (Hz) 30-60 30 15-30 30-60 60-120+ 60-120 20-50
Lag (perceptual) Low Low Low Low Very Low Low Medium
Maximum people tracked Many Typically 4 Tradeoff with volume Tradeoff with volume Many 4 Many
Person detection Yes Yes Not usually N/A Yes Yes N/A
ID Stability Medium Medium to High Low High High Medium to High Very High
3D Tracking Yes Yes No Yes Yes Yes Yes
Skeletal Tracking No Yes No No Yes Yes No
Must carry / wear something No No No Yes Yes No Yes
Position accuracy High High Varies greatly Medium to High Very high Very high Medium
Occlusion resistance High in multi-imager nets Low Low Low High Medium Requires multiple tags
Visible light sensitivity Minimal Minimal Yes Yes Some High None
IR light sensitivity Depends on imager Yes Depends on imager Depends on Imager Often Not usually No
Costume sensitivity Must be humanoid Must be humanoid None Must show tag Must wear marker suit Must be humanoid None
Multiple imager types Yes No Yes Yes Yes Yes n/a
Typical setup time Medium Very low Low to Medium Low to Medium Medium to High Medium to High Medium to High
App integration complexity Low Low Low to Medium Low to Medium Medium to High Medium to High Low to Medium
Open source software Yes Usually Usually Usually No No No
Off-the-shelf parts Yes Yes Yes Yes No No No
Typical system cost $$ $ $ $ $$$$ $$$$ $$$

(Feel free to email us with additions and corrections to this chart!)