Changes between Version 20 and Version 21 of Other/Summer/2025/CityOS

Timestamp:

Jul 17, 2025, 5:28:27 AM (40 hours ago)

Author:

sh1746

Comment:

—

Other/Summer/2025/CityOS

@@ -16 +16 @@
 
 || [[Image(CityOS-Design Flowchart.png, height=500vw)]] ||  The data collection is being done from the Rutger's Winlab Camera which is pointed into the adjacent parking lot. It collects video footage daily from 7 am to 7 pm.  The video processing is where the video footage is analyzed for information. In our case, it is checking each of our 10 parking spots for availability. When a vehicle parks in any of the spots, it will return data that this spot has been occupied. We accomplished this functionality using OpenCV and YOLOv8.  The video formatting is where the video data is formatted and aggregated into a CSV file. Within a line, it displays the spotID number, Month, Day, Year, Hour, Minute, Second, and the status of whether the spot is occupied or free.  The prediction model is where the aggregated data is imported in order to train and teach a machine learning model. In our project, we are using a Random Forest model, which will take the data and use it to predict the occupancy given a spotID and a time.  The user interface is where people can interact with our systems. We are planning to implement a large interactive UI where users are able to see a mock replica of the parking lot and are able to then select and visualize parking lot occupancy over the days and weeks. ||
-|| The graphs on the right is our current accuracy between the predicted parking occupancies generated by the machine learning model in comparison to the actual parking occupancies of that day. || [[Image(PredictorModel.png, height=300vw)]] ||
+|| 
+
+|| 
+{{{#!td 
+The graphs on the right are the comparisons between the predicted parking occupancies generated by the machine learning model in comparison to the actual parking occupancies of that day. It is separated by each parking spot. The type of model we decided to use for this project is called a Random Forest model. This model comprises of much smaller models called decision trees. These decision trees act as a series of yes/no questions which is used to categorize data. An example would be:
+
+1. "Is this fruit yellow?"
+  a. if yes, "Is this fruit curved?"
+    i. if yes, "It's probably a banana"
+    ii. if no, "It's probably a lemon" 
+  b. if no, "Is this fruit red?"
+
+A single decision tree will try to predict by asking these kinds of questions to break down the data step-by-step.
+}}}
+|| [[Image(PredictorModel.png, height=300vw)]] ||
 
 === Weekly Progress