Introduction to AI Traffic Counting Camera

The Vehicle Flow StatisticsAI CameraIntelligent devices that use artificial intelligence technology and advanced image recognition algorithms to monitor and count traffic flow on roads.

Target Detection: The camera captures real-time video images of the road, uses deep learning algorithms and image processing techniques to identify vehicles in the video frames. These algorithms have undergone extensive training and can accurately identify various types of vehicles, regardless their color, shape, or type. Even in complex traffic scenarios, such as when vehicles are blocking each other or when the light changes, the camera can maintain high accuracy rate.

Vehicle Tracking: Once a vehicle is detected, the camera tracks the vehicle's movement across different frames, recording its trajectory. This typically involves multi tracking algorithms, such as SORT and DeepSORT, combined with Kalman filters for state prediction and update. This allows the camera to accurately track each vehicle's driving, ensuring that the same vehicle is neither counted twice nor missed in the entire video segment.

Traffic Statistics: When a vehicle passes through a pre-set monitoring area or coil, the algorithm detects the change in the vehicle's position and uses this information to count the number of vehicles, their speed, and direction. A virtual coil is a line or curve defined in the road area of the video frame, usually perpendicular to the lane direction. When a vehicle crosses the virtual coil, it is counted.

High Precision Statistics: It can provide accurate traffic flow data, offering reliable support for traffic management departments and related research institutions. Compared to traditional manual counting simple sensor-based methods, the AI camera's results are more precise, significantly reducing errors.

All-Weather Operation: It is not affected by environmental factors such as and light. Whether it's day or night, sunny or rainy, the camera can operate normally, continuously monitoring and counting the traffic flow on the road.

It can obtain real-time traffic flow information and transmit the data to the traffic management center or other related systems. This enables managers to promptly grasp road conditions, make quick decisions, and implement appropriate measures.

Vehicle Type Recognition: In addition to counting the number of vehicles, it can also identify different types of vehicles, such as small cars, large buses, trucks, and motorcycles. This provides a more comprehensive analysis of the traffic composition and characteristics, offering more detailed information for traffic planning and management.

Traffic Management: Assists traffic management departments in real-time monitoring of road traffic flow. Based on traffic data, it timely adjusts duration of traffic signals, optimizes traffic organization at intersections, improves road passage efficiency, and alleviates traffic congestion.

Provides crucial data support for urban departments, which is used to evaluate road capacity, plan new road construction, and optimize public transportation routes.

Commercial Decision-making: For commercial venues such as shopping malls, supermarkets, and parking lots, AI camera traffic counting can assist managers in understanding customer arrivals and departures, optimizing parking lot management, and making commercial arrangements more effectively.