1.Car counting script is able to accuratly quantify the amount of car and trucks in differente ways in real time.
2. Efficient car counting was obtained adopting multiple angle camera and position.
Traffic congestion and pollution in big cities are significant challenges impacting urban quality of life, public health, and economic productivity. Rapid urbanization, increasing car ownership, and inadequate public transportation systems contribute to overcrowded streets and long travel times. Furthermore, vehicle emissions exacerbate air pollution, leading to respiratory illnesses, climate change impacts, and environmental degradation.
Car 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.
- Data: Camera from Rio de Janeiro avenues.
- Data Collection Suggestion: Strategic camera position in the main avenues of the city.
- Stop data collection: Urban region with superposition of car counting.