Updated README

processing/README.md

@@ -4,20 +4,49 @@
 conda env create -f conan_windows.yml
 conda activate conan_windows_env
 ```
-
+## Usage 
-### OpenPose
+Run [ConAn_RunProcessing.ipynb](ConAn_RunProcessing.ipynb) to extract all frames from video and run processing models. 
-### RT-Gene
+### Body Movement
-- Run [processing/install_RTGene.py](/processing/install_RTGene.py)
+For body movement detection we selected [OpenPose](https://github.com/CMU-Perceptual-Computing-Lab/openpose). For our case, we used the 18-keypoint model, 
-- [OPTIONAL] Provide camera calibration file calib.pkl 
+which takes the full frame as input and jointly predicts anatomical keypoints and a measurement 
-- Provide maximum number of people in the video
+for the degree of association between them.<br>
-### JAA-Net
+If you're using this processing step in your research please cite: 
-### AVA-Active Speaker
-### Apriltag
-
-[https://www.wikihow.com/Install-FFmpeg-on-Windows](https://www.wikihow.com/Install-FFmpeg-on-Windows)
-### Training 
 ```
-conda install -c anaconda cupy
+@article{8765346,
-conda install -c anaconda chainer
+  author = {Z. {Cao} and G. {Hidalgo Martinez} and T. {Simon} and S. {Wei} and Y. A. {Sheikh}},
-conda install -c anaconda ipykernel
+  journal = {IEEE Transactions on Pattern Analysis and Machine Intelligence},
+  title = {OpenPose: Realtime Multi-Person 2D Pose Estimation using Part Affinity Fields},
+  year = {2019}
+}
 ```
+### Eye Gaze 
+For eye gaze estimation we selected [RT-GENE](https://github.com/Tobias-Fischer/rt_gene). In addition to feeding each video frame to the model, 
+we also input a version of the frame where the left side and the right side are wrapped together. 
+This enables us to detect when a person moves over the edge of the video, as none of the models account for this. 
+As this is a single frame estimation, we then track all subjects throughout the video using a minimal euclidean distance heuristic. <br>
+<br>
+If you're using this processing step in your research please cite: 
+```
+@inproceedings{FischerECCV2018,
+author = {Tobias Fischer and Hyung Jin Chang and Yiannis Demiris},
+title = {{RT-GENE: Real-Time Eye Gaze Estimation in Natural Environments}},
+booktitle = {European Conference on Computer Vision},
+year = {2018},
+month = {September},
+pages = {339--357}
+}
+```
+Notes: 
+- Before using [process_RTGene.py](process_RTGene.py) you need to run [install_RTGene.py](install_RTGene.py)!
+- [OPTIONAL] You can provide a camera calibration file calib.pkl to improve detections. 
+- You need to provide maximum number of people in the video for the sorting algorithm. 
+### Facial Expression
+Under construction 
+### Speaking Activity 
+Under construction 
+### Object Tracking
+We assume that you are most likely able to define your own study procedure, 
+therefore we decided to simplify object tracking by employing the visual fiducial system [AprilTag 2](https://github.com/AprilRobotics/apriltag), 
+where the tag positions are extracted with their tailored detector.
+
+Note: For Windows we use [pupil_apriltags](https://github.com/pupil-labs/apriltags).