Added

README.md

@@ -30,13 +30,13 @@ This repository is for our work:
 </tr>
 </table>
 
-pushing behavior in this article is defined as an unfair strategy that some pedestrians use to move quickly and enter an event faster.
+Pushing behavior in this article is defined as an unfair strategy that some pedestrians use to move quickly and enter an event faster.
 #### The Architecture of `DL4PuDe`
 
 `DL4PuDe` mainly relied on the power of EfficientNet-B0-based classifier, RAFT and wheel visualization methods.
 
 <img src="./files/framework1.png"/>
-Kindly note, we use the [RAFT repository](https://github.com/princeton-vl/RAFT) for optical flow estimation in our project.
+Kindly note that we use the [RAFT repository](https://github.com/princeton-vl/RAFT) for optical flow estimation in our project.
 
 **Example**
 <table border="0" width="100%" align="center">
@@ -78,20 +78,20 @@ python3 run.py --video [input video path]
 #### Demo 
 <br/>
 
->Run the follwing command
+>Run the following command
 
 ```   
 python3 run.py --video ./videos/150.mp4  --roi 380 128 1356 1294 --patch 3 3 --ratio 0.5  --angle 0
 ```  
-> Then, you  will see the following details.
+> Then, you will see the following details.
 
 <img src="./files/run.png"/>
 
 > When the progress of the framework is complete, it will generate the annotated video in the framework directory. Please note that the "150 annotated video" is available on the directory root under the "150-demo.mp4" name.
 
 #### Experiments Videos
 
-The original experiments videos that are used in this work, are available through the [Pedestrian Dynamics Data Archive hosted](http://ped.fz-juelich.de/da/2018crowdqueue) by the Forschungszentrum Juelich. Also, the undistorted videos are available by [this link.](https://drive.google.com/drive/folders/16eZhC9mnUQUXxUeIUXd6xwBU2fSf3qCz?usp=sharing) 
+The original experiments videos that are used in this work are available through the [Pedestrian Dynamics Data Archive hosted](http://ped.fz-juelich.de/da/2018crowdqueue) by the Forschungszentrum Juelich. Also, the undistorted videos are available by [this link.](https://drive.google.com/drive/folders/16eZhC9mnUQUXxUeIUXd6xwBU2fSf3qCz?usp=sharing) 
 
 #### CNN-based Classifiers