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COSBOS: COlor-Sensor-Based Occupancy Sensing View COSBOS: COlor-Sensor-Based Occupancy Sensing on File Exchange

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Overview

This is the software package for using color sensors and perturbation-modulated light (color-controllable fixtures required) to estimate the occupancy distribution in an indoor space. It corresponds to these papers:

[1] Quan Wang, Xinchi Zhang, Kim L. Boyer, "Occupancy distribution estimation for smart light delivery with perturbation-modulated light sensing", Journal of Solid State Lighting 2014 1:17, ISSN 2196-1107,
doi:10.1186/s40539-014-0017-2.

[2] Quan Wang, Xinchi Zhang, Meng Wang, Kim L. Boyer, "Learning Room Occupancy Patterns from Sparsely Recovered Light Transport Models", 22nd International Conference on Pattern Recognition (ICPR), 2014.

[3] Quan Wang, Xinchi Zhang, Kim L. Boyer, "3D Scene Estimation with Perturbation-Modulated Light and Distributed Sensors", 10th IEEE Workshop on Perception Beyond the Visible Spectrum (PBVS).

[4] Xinchi Zhang, Quan Wang, Kim L. Boyer, "Illumination Adaptation with Rapid-Response Color Sensors", SPIE Optical Engineering + Applications, 2014.

The YouTube video introducing this technique:

Contents

This package includes:

  1. The code for Light Transport Model (LTM) recovery, both overdetermined and underdetermined. See 'LTM_Recovery/demo_LTM.m' for a demo. This work is described in [2].

  2. The code for 3D scene estimation with light blockage model and wall-mounted sensors. See 'BlockageModel/demo_Blockage.m' for a demo. This work is described in [1] and [3].

  3. The code for floor-plane occupancy mapping with light reflection model and ceiling-mounted sensors. See 'ReflectionModel/demo_Reflection.m' for a demo. This work is described in [1].

More information:

Copyright

Copyright (C) 2014 Quan Wang <wangq10@rpi.edu>,
Signal Analysis and Machine Perception Laboratory,
Department of Electrical, Computer, and Systems Engineering,
Rensselaer Polytechnic Institute, Troy, NY 12180, USA

You are free to use this software, but we would appreciate it if you can cite our papers.

Acknowledgement

This work was supported primarily by the Engineering Research Centers Program (ERC) of the National Science Foundation under NSF Cooperative Agreement No. EEC-0812056 and in part by New York State under NYSTAR contract C090145.