What is Vehicle Counting with Computer Vision?
Intelligent vehicle counting plays an important role in the transportation systems of smart cities. Computationally efficient and reliable deep learning algorithms can classify the type of vehicle and count them in real-time using the video of conventional surveillance cameras.
Vehicle counting is a key strategy of traffic analytics and can be used on highway and urban roads under different weather and traffic conditions. A vehicle counting system can be permanently installed using already or newly installed surveillance cameras.
Key Features of Vehicle Counting
Robust deep learning algorithms achieve high accuracy in object detection and classification tasks.
- Detect and classify multiple vehicle types (truck, bus, bicycle, car) automatically and contactless.
- Track multiple detected objects and count them in real-time as they pass a specific area.
- Aggregate counting data over time with custom logic and send it to third-party systems.
- Edge AI allows full data privacy and robustness with on-device machine learning for image processing.
Value of Vehicle Counting Applications
Vision-based vehicle counting provides a highly scalable method to count vehicles at multiple remote locations.
- Fully automated traffic analysis in real-time, applicable for large-scale use.
- Detect anomalies and dangerous events effectively, using the video of common CCTV cameras.
- Insights to detect and monitor peak hours, bottlenecks and compare different locations.
- Quantify and track changes over time and how measures (to reduce traffic congestion or lower urban traffic to reduce emissions) effectively impact traffic flow.