Case study

Optimizing Urban Mobility with Computer Vision and Edge Computing Parking Solution

About Client

A growing player in the smart mobility and urban infrastructure sector, building platforms that help people quickly locate parking spots.
The company’s mission and vision revolve around reducing search time, improving traffic flow, and enhancing the daily parking experience via real-time data, GPS, and intelligent routing technology.

Problem STATEMENT

During our initial discussions, our client highlighted multiple challenges in enabling efficient, real-time parking for users in urban areas.

Lack of real-time visibility: Users had no access to accurate, real-time data on available parking spots, leading to long search times and traffic congestion.
Manual parking monitoring: The client relied heavily on manual checks or outdated systems, which made parking space detection inefficient and inconsistent.
No intelligent detection system: There was no automated solution in place to process live images or video feeds and identify unoccupied parking spaces reliably.
Poor user experience: Due to delayed or inaccurate updates, users often reached locations only to find no parking available, impacting convenience and satisfaction.

Solution

To solve the client’s parking challenges, our engineers built a smart parking system that runs on edge devices and uses computer vision for real-time detection. Here’s what we implemented:

Data Pipeline Setup
Our developers used a mix of manual and Roboflow auto-labeling to prepare training data. We also added different lighting and weather conditions to the dataset so the model could work well in all environments.
YOLOv8n + Tracker Integration
We chose the lightweight YOLOv8n model for object detection and combined it with ByteTrack to track vehicles across video frames smoothly and accurately.
Depth Estimation with Depth-Anything-v2
Our team added a depth estimation model that helps measure real-world distances from single-camera input. This helped us apply parking rules based on spacing and location.
Optimized for Raspberry Pi
The model was converted to ONNX, TFLite, and NCNN formats to run efficiently on a Raspberry Pi. We also used quantization and threading to improve speed without needing heavy hardware.
Sensor & Location Mapping
Our engineers connected a camera, GPS, and compass and synced the data using timestamps. This allowed the system to tag each parking detection with real-time location and direction data.

Technical Implementation

Data Collection & Annotation
Frames from street footage were extracted and manually labeled using Roboflow to mark parking spaces. Data was augmented to handle various lighting and weather conditions.
Model Training
A lightweight YOLOv8n model was trained using transfer learning for better accuracy on custom classes. The process was managed in the Ultralytics YOLO environment for consistency.
Real-Time Inference
YOLOv8n was paired with ByteTrack to track vehicles across frames. Post-processing logic helped smooth detections and reduce flickering.
Depth Estimation
YOLOv8n was paired with ByteTrack to track vehicles across frames. Post-processing logic helped smooth detections and reduce flickering.
Edge Deployment
The model was optimized and converted to formats like ONNX and TFLite for deployment on Raspberry Pi, enabling fast, low-power inference.
Geo-Spatial Integration
GPS and compass modules captured real-time location and direction, allowing each detection to be mapped accurately on real-world coordinates.

Technical Architecture

Business Impact

Reduced Costs

By leveraging edge computing, operational costs were reduced by 30%, while labor expenses saw a drop of 20-25%, making the solution more cost-effective.

Improved Accuracy

The integration of YOLOv8n and depth estimation increased detection accuracy by 15-20%, which helped optimize space utilization by 30%, ensuring better use of available parking.

Scalable Solution

The system proved to be highly scalable, with hardware costs 50% lower than traditional cloud-based systems, allowing for future growth without significant expense.

Faster Parking Search

Thanks to mobile app integration, users now experience up to a 40% reduction in the time spent searching for parking, improving the overall user experience.

Optimized Space Use

By integrating GPS and data, the system helped optimize parking space utilization, adding 10-15% more available spots for users.

Better Resource Allocation

Actionable insights generated by the system enabled more efficient resource allocation, improving overall operations by 15-20%.

Smart City Support

The solution also supports smart city initiatives by improving urban mobility, reducing congestion, and providing data that can be used for future urban planning.

In the end, we helped the client turn a major urban challenge into a smart, scalable solution. With real-time detection, GPS integration, and edge deployment, the parking experience is now faster, smoother, and way more reliable for users. More than just solving technical issues, the solution supports the bigger goal—making city mobility smarter and everyday commutes less stressful.

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Optimizing Urban Mobility with Computer Vision and Edge Computing Parking Solution

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