We're interested in seeing how traffic levels have changed between Jan 2019 - a normal year, and Jan 2020 - a COVID year in Singapore.
The hypothesis is that the levels have dropped, and we're determined to find out through looking at the traffic cameras and computer vision.
- Data
- Flow of project
- Part 1 - Exploring Data API
- Part 2 - Data Extraction
- Part 3 - Data Cleaning and Transformations
- Part 4 - Object Detection using YOLOv5
- Part 5 - Visualizationa and Data Analysis
- Acknowledgments
The data is from Singapore government provided API at https://data.gov.sg/dataset/traffic-images .
- Explore Singapore's traffic camera API and map the locations of the cameras (Part I)
- Make two one-month API calls to collect the initial data (Part II)
- Collecting massive amounts of image data from API (Part III)
- Prepare for GPU-enabled object detection (Part III)
- Using computer vision to count traffic in 2019 and 2020 (Part IV)
- Compare the traffic between two years (Part V)
- Explore the traffic camera API from Singapore's Data.gov.sg
- Map the locations of the traffic camera in Singapore
- Identify the best camera to focus on for data collection
- Collect two years' worth of traffic image JSON
- Combine the eventual DataFrame
- Filter for camera ID 1709 only
- Export DataFrame for Part III
- Write a function to download images into drive
- Execute the function concurrently
- Prepare for OpenCV GPU execution in Part IV
- Test object detection on one image
- Detect number of objects in all images
- Updating the DataFrames with the vehicle count
- Transform and simplify the DataFrames
- Plot the respective num_cars over time
- Tribe Academy