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--- |
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task_categories: |
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- object-detection |
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language: |
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- en |
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tags: |
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- computer vision |
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- code |
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- python |
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- traffic |
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- singapore |
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- roadway |
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pretty_name: Traffic Images for Object Detection |
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size_categories: |
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- 10K<n<100K |
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--- |
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# Traffic Image Data Extraction Through Singapore Government API |
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## Description |
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The Singapore government offers real-time images from traffic cameras across the nation through its API. This dataset compiles a comprehensive image dataset in the form of a DataFrame by extracting data for the month of January 2024 from 6 pm to 7 pm each day using the API. |
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Below are sample images from the dataset: |
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<div style="display: flex; justify-content: space-around;"> |
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<img src="76.jpg" alt="Sample image from the data" width="600"/> |
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<img src="61.jpg" alt="Sample image from the data" width="600"/> |
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</div> |
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## Use Cases |
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The resulting dataset will facilitate easy integration into various use cases including: |
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### Object Detection |
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Utilize the dataset for training object detection models to identify and analyze vehicles, pedestrians, and other objects in the traffic images. |
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### Traffic Trend Analysis |
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Leverage time-series analysis to identify and analyze traffic trends over specific periods. This can provide valuable insights into peak traffic times, congestion patterns, and potential areas for infrastructure improvement. |
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### Road Safety Assessment |
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Implement computer vision algorithms to assess road safety by analyzing traffic images for potential hazards, unusual road conditions, or non-compliance with traffic rules. This use case aims to enhance road safety monitoring and contribute to the development of intelligent transportation systems. |
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## Dataset Details |
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The dataset will comprise the following columns: |
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- **Timestamp**: Date and time of the image acquisition from LTA's Datamall. |
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- **Camera_ID**: Unique identifier assigned by LTA to each traffic camera. |
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- **Latitude**: Geographic coordinate of the camera's location (latitude). |
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- **Longitude**: Geographic coordinate of the camera's location (longitude). |
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- **Image_URL**: The traffic image fetched from the Image_URL provided by the API. |
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- **Image_Metadata**: Metadata of the image file including height, width, and MD5 hash. |
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## Limitations of my Dataset |
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The Dataset due to limited computational capability has data of only one month and 1 hour for each day. |
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Fetching large data (such as a year) would help in analysing the macro trends and significant patterns. |
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## API Documentation |
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For more details on accessing the traffic camera images, visit the [API Documentation](https://beta.data.gov.sg/collections/354). |
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## Use Case |
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Refer to the attached traffic_object_detection.py file to see how I used a pretrained YOLO model to detech cars and trucks. Further I generated traffic insights using an interactive streamlit dashboard (code not on HuggingFace). |
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Below is a sample output of the YOLO model |
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<img src="Picture1.png" alt="Sample image from the data" width="600"/> |
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Here are the snippets of my Dashboard: |
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<div style="display: flex; justify-content: space-around;"> |
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<img src="sd1.png" alt="Sample image from the data" width="700"/> |
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<img src="sd_2.png" alt="Sample image from the data" width="700"/> |
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</div> |
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Version 2.0 of the dataset and analysis coming soon! |
