Find Your Spot

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Inspiration

The inspiration for Find Your Spot, was born out of the frustration of endlessly circling parking lots in search of a spot, especially during busy times. As urban areas become increasingly congested, finding parking spots quickly and efficiently has become a major hassle for many. This project aimed to address this problem by leveraging the power of AI to streamline the parking process.

What it does

Real-Time Parking Availability: "Find Your Spot" uses AI-powered computer vision to analyse live video feeds from parking lots and determine the availability of parking spaces in real time.
User-Friendly Interface: Users can access the "Find Your Spot" platform through a mobile app or web interface, where they can view a map of parking lots and see which spaces are available.
Instant Notifications: The system sends push notifications to users when a parking space becomes available in their desired location, helping them secure a spot quickly and efficiently.
Optimized Routing: By providing users with up-to-date information on parking availability, "Find Your Spot" helps drivers navigate to the nearest available parking space, minimising time spent searching for parking.
Data Analysis: The platform collects data on parking usage and trends over time, allowing parking lot operators to optimise their operations and improve the overall parking experience for users.

How we built it

Data Collection and Labelling: We gathered a dataset of parking lot images and manually labelled them to train our AI model.
Model Training: Using machine learning algorithms and frameworks like TensorFlow, we trained our model to recognize vehicles and identify empty parking spaces.
Algorithm Optimization: We fine-tuned our algorithms to work efficiently in various conditions, such as different lighting and weather scenarios.
Integration: We integrated our AI model with a backend server and developed a user-friendly frontend interface to display parking availability to users.

Challenges we ran into

One of the main challenges we encountered was ensuring the robustness and reliability of our system in different environmental conditions. We had to fine-tune our algorithms to handle variations in lighting, weather, and vehicle types. Additionally, integrating the AI model with the backend server and frontend user interface posed technical challenges that required creative solutions.

Accomplishments that we're proud of

Despite these challenges, the journey of building "Find Your Spot" was incredibly rewarding. We are proud to have created a solution that has the potential to make a positive impact on urban mobility and improve the overall parking experience for millions of people.

Improved Parking Management: Provided valuable insights to parking lot operators by analysing data on parking usage patterns, helping them optimize parking space allocation and improve overall efficiency.
Reduced Traffic Congestion: Contributed to reducing traffic congestion and emissions by enabling drivers to quickly find parking spots, minimizing the time spent circling and searching for available spaces.
Innovative Solution: Developing an AI-driven parking system that demonstrates the potential of technology to address urban mobility challenges and enhance the parking experience for drivers is a significant technological achievement.
High Accuracy: Achieved a high level of accuracy in detecting parking space occupancy using AI and computer vision technologies, ensuring reliable and precise information for users.

What we learned

In our journey of developing Find Your Spot, we dived deep into understanding the challenges in the urban parking system. By recognizing the complexities of parking management and the frustration it causes, we were driven to innovative and creative solutions. This process taught us the importance of problem solving and thinking outside the box to address real-world issues effectively.

Throughout the development of "Find Your Spot," we gained valuable insights into machine learning, computer vision, and real-time data processing. We learned how to train AI models to detect and track vehicles, optimize algorithms for speed and accuracy, and integrate our solution with existing infrastructure.

What's Next for Find Your Spot

Looking ahead, we have several features to be implemented for further development and enhancements:

Scalability: We aim to scale our solution to accommodate larger parking lots, multiple parking levels, and even smart city implementations. This involves refining our algorithms for robustness and efficiency.
Integration: We plan to integrate additional features such as predictive analytics for parking availability, reservation systems, and seamless payment options to provide a comprehensive parking solution.
Community Engagement: Engaging with local communities and municipalities is crucial for the success of our project. We'll seek feedback, collaborate with stakeholders, and implement our user-friendly parking solutions in their community.