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17 changes: 14 additions & 3 deletions docs/project-4/Small_Object_Detection/Documentation.md
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Expand Up @@ -66,8 +66,19 @@ deviation_penalty = 0.7
- Deployed an interactive application using **Streamlit** to visually showcase the model’s performance.
- Users can input real-time data and view detection results through a visual interface.



### Extraction
- Developed a data logistics system using MongoDB to manage and store large datasets efficiently.
- Created a smart query system to interact dynamically with a computer vision system for specific data retrieval.
- Designed a high-level architecture to automate and streamline data logistics processes.

### Flow & Heatmap Analysis
- Generated crowd density heatmaps using Python libraries for intuitive visualisation of populated areas.
- Designed a flow map to illustrate crowd movement history over time, enabling better crowd management insights.

### Object Detection
- Built an object detection system using YOLO and OpenCV to track individuals entering, exiting, and moving within a stadium during a soccer match.
- Integrated object detection data with heatmap analysis to create a visual representation of crowd behaviour in 3D.

## Performance Analysis
The performance analysis revealed key insights into the challenges of deploying advanced detection models like YOLOv8 and SAHI in real-world scenarios:

Expand Down Expand Up @@ -129,7 +140,7 @@ Future work will focus on optimizing the pipeline to balance accuracy and execut
## Contributors
- **MD Tajish Farhan**
- **Sahil Guglani**

- **Damien Peters**


## Example Code
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