- The car parking counting script is able to accurately identify parking spot vacancies in real time.
- An efficient car parking network can be established to obtain data in real time. This reduces traffic congestion.
Car parking counting spots in real-time offers an innovative approach to managing urban parking in big cities. Benefits:
1. Real-time data on available parking spaces using cameras.
2. Clear identification of parking spots.
3. Decreasing traffic congestion, fuel consumption, and emissions.
4. Minimization of spent time searching for parking.
Car parking counting using computer vision contributes to improved traffic congestion management, offering numerous benefits to various stakeholders (city authorities, residents).
1. Real-time and accurate data on vehicle numbers
2. Optimize traffic signal timings and identify congestion hotspots.
3. Reduction of waiting times, fuel consumption, and emissions.
4. Shorter travel times and enhanced safety.
- Python
- Yolov
- ByteTrack
- API.
1. Camera Calibration: Calibrate the existing urban cameras for accurate vehicle detection and counting, accounting for perspective distortion and other factors.
2. Data Transmission: Establish a secure and reliable communication channel for real-time video feed transmission between cameras and the cloud infrastructure using Apache Kafka for streaming and data ingestion.
3. Cloud Storage: Store incoming video feeds in a cloud storage service, such as Amazon S3 or Google Cloud Storage, for further processing and archiving.
4. Vehicle Detection: Implement a computer vision algorithm, such as a convolutional neural network (CNN), using cloud-based machine learning platforms like TensorFlow, PyTorch, or Google Cloud AutoML to detect vehicles in the video feeds.
5. Car Counting: Utilize the vehicle detection results to count cars, either by tracking or by analyzing sequential frames.
6. Data Aggregation: Consolidate the car counting data from multiple cameras or locations using cloud-based data processing tools like Apache Spark or Google Cloud Dataflow.
7. Data Analysis: Analyze the aggregated data to identify traffic patterns, congestion hotspots, and other insights that can inform traffic management strategies using cloud-based analytics services like Amazon Redshift or Google BigQuery.
8. Data Visualization: Present the processed data in an accessible format, such as a dashboard or map, for traffic management authorities to use, leveraging cloud-based visualization tools like Tableau or Google Data Studio.