Smart Car Parking System
Converting unstructured camera feeds into real-time slot availability using computer vision models.
Included in my resume project highlights as a core computer-vision build, where I contributed as Team Lead and CV Developer from prototype design through occupancy detection implementation.
Occupancy Detection
Computer Vision
Python
OpenCV
C/C++
Image Processing
System Architecture
- Capture Layer: Continuously ingests parking-lot camera frames.
- Region Layer: Maps predefined slot coordinates for focused analysis.
- Detection Layer: Applies image-processing/CV logic (edge detection, thresholding) to infer occupancy based on pixel density.
- Presentation Layer: Displays visual availability status for operator decision support.
Key Challenges Solved
- Lighting Variability: Outdoor environments caused inconsistent frame characteristics. Tuned preprocessing models to stabilize inference.
- Perspective Distortion: Refined slot mapping and validation overlays for geometric reliability.
- False Signals: Shadows produced noise. Applied rule-based checks and masking to reduce spurious detections.
Role & Resume-Aligned Contributions
- Project Leadership: Led execution planning, module coordination, and milestone-based delivery as team lead.
- CV Implementation: Built occupancy detection logic using image-processing and computer-vision workflows.
- Skill Application: Applied core stack from resume highlights — Python, OpenCV, and C/C++ fundamentals.
- Prototype Outcome: Delivered a practical smart mobility proof-of-concept for real-time parking visibility.
Urban Mobility Impact
By translating raw visual data into binary occupancy logic, this prototype acts as a critical scalable foundational block for modern Smart City infrastructure.
Real-time
Feedback Loop
Zero
Hardware per slot
Scalable
Software Base