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EagleSense: Tracking People and Devices in Interactive Spaces using Real-Time Top-View Depth-Sensing
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README.md

EagleSense

OVERVIEW | PUBLICATIONS | LICENSE

INSTALL | BUILD | RUN

OVERVIEW

EagleSense is a top-view tracking system that leverages a depth-infrared hybrid sensing pipeline for real-time human activity and device recognition. It provides a minimalistic RESTful API that can be used by ubicomp applications (e.g., proxemic-aware or cross-device systems) in interactive spaces.

EagleSense

VIDEO

PUBLICATIONS

  1. EagleSense: Tracking People and Devices in Interactive Spaces using Real-Time Top-View Depth-Sensing. In Proceedings of the 35th Annual ACM Conference on Human Factors in Computing Systems (CHI'17).

LICENSE

EagleSense: Tracking People and Devices in Interactive Spaces using Real-Time Top-View Depth-Sensing

Copyright © 2016 Chi-Jui Wu

This program is free software: you can redistribute it and/or modify it under the terms of the GNU General Public License as published by the Free Software Foundation, either version 3 of the License, or (at your option) any later version.

This program is distributed in the hope that it will be useful, but WITHOUT ANY WARRANTY; without even the implied warranty of MERCHANTABILITY or FITNESS FOR A PARTICULAR PURPOSE. See the GNU General Public License for more details.

You should have received a copy of the GNU General Public License along with this program. If not, see http://www.gnu.org/licenses/.

INSTALL

  1. Install Git (and Git Desktop).

  2. Clone EagleSense.

    cd /your_workspace/
    git clone https://github.com/cjw-charleswu/eaglesense
  3. Install Visual Studio with Visual C++.

  4. Install Microsoft Kinect v2 SDK.

  5. Download Boost C++ Libraries windows binaries.

  6. Download OpenCV 3 windows binaries.

  7. Install Python 3 (The Anaconda distribution will contain most of the Python requirements.), also see requirements.txt.

  8. Install XGBoost.

    Follow this installation guide. Download XGBoost.dll and then install the Python package.

Project structure

eaglesense/
    +-- data/
    ¦   +-- topviewkinect/
    +-- eaglesense/
    ¦   +-- server/
    ¦   +-- topviewkinect/
	+-- models/
	+-- config.json
	+-- setup.py
	+-- server.py
	+-- topviewkinect.py
	+-- requirements.txt
	+-- README.md

BUILD

Initialize project directories

```bash
cd /path/to/eaglesense/
python setup.py
```

EagleSense Tracking System

  1. Set the following system environment variables.

    • BOOSTCPP=/path/to/boost
    • PYTHON3=/path/to/python3 (or /path/to/anaconda)
    • OPENCV3=/path/to/opencv3
    • Add %OPENCV3%\build\x64\vc15\bin, %PYTHON3%, %PYTHON3%\Scripts to PATH.
  2. Open the EagleSense project /path/to/eaglesense/eaglesense/topviewkinect/topviewkinect.sln.

  3. Set solution configuration to Release and x64.

  4. In the solution explorer, right click the topviewkinect project then select Properties. Include the following fields as needed. Make sure the versions are correct.

    • VC++ Directories --> Include Directories

      $(KINECTSDK20_DIR)\inc;$(VC_IncludePath);$(WindowsSDK_IncludePath);

    • VC++ Directories --> Library Directories

      $(KINECTSDK20_DIR)\lib\x64;$(VC_LibraryPath_x64);$(WindowsSDK_LibraryPath_x64);$(NETFXKitsDir)Lib\um\x64

    • C/C++ --> General --> Additional Include Directories

      include;$(OPENCV3)\build\include;$(BOOSTCPP);$(PYTHON3)\include;$(PYTHON3)\Lib\site-packages\numpy\core\include

    • Linker --> General --> Additional Library Directories

      $(OPENCV3)\build\x64\vc14\lib;$(BOOSTCPP)\lib64-msvc-14.0;$(PYTHON3)\libs;$(PYTHON3)\Lib\site-packages\numpy\core\lib;%(AdditionalLibraryDirectories)

    • Linker --> Input --> Additional Dependencies

      opencv_world340.lib;kinect20.lib;python36.lib;_tkinter.lib;npymath.lib;%(AdditionalDependencies)

  5. Open Build then Build solution (CTRL + SHIFT + B).


RUN

Tracking System

  1. Configure the EagleSense top-view tracking system via config.json in the EagleSense root directory.

    {
        "tracking": {
            "framerate": 1,
            "orientation_recognition": 1,
            "interaction_recognition": 1,
            "restful_connection": 0
        },
        "interaction_model": "standingtablet-xgboost.model",
        "restful_server": {
            "address": "localhost",
            "port": 5000
        },
        "data": {
            "depth": 1,
            "infrared": 1,
            "color": 0
        }
    }
  2. Run

    cd eaglesense/
    
    # Option 1: Python script
    python topviewkinect.py
    
    # Option 2: Executable
    ./eaglesense/topviewkinect/x64/Release/topviewkinect.exe

    Usage:

    General:
      -v [ --version ]                   	Version
      -h [ --help ]                      	Help
    
    Advanced:
      -r [ --replay ]                       Replay
      -c [ --capture ]                      Capture
      -p [ --postprocess ]                  Postprocess
      -f [ --features ]                     Postprocess (Features only)
      -d [ --dataset_id ] arg               Dataset ID (Required for advanced options)
      -n [ --dataset_name ] arg (=Untitled) Dataset name
    
  3. JSON Data

    {
    	"timestamp": 1450123752478,
    	"skeletons": [
    		{
    			"id": 0,
    			"head": {
    				"x": 208,
    				"y": 165,
    				"z": 65,
    				"orientation": 90,
    			},
    			"activity": "tablet",
    			"activity_tracked": 1
    		}
    	]
    }

RESTful Server

  1. Run

    cd eaglesense/
    python server.py

    Usage:

    python server.py [-h] [--host HOST] [--port PORT]
    
    General:
      -h [ --help ]	Help
      --host HOST  	Server IP address
      --port PORT  	Server port number
    

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