Industrial-grade AI Video Recognition Host

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Smart Parking Occupancy Detection and Real-Time Guidance

• Industrial-grade AI video recognition hosts deployed onsite analyze parking lot video in real time, automatically recognizing stall occupancy, vehicle entry/exit behavior, and license plate data, then synchronizing results to the backend CMS for integrated display and data governance.

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Unmanned Parking Management

• The system uses ANPR at the edge for real-time computing, automatically recording vehicle entry/exit times, matching plate data, and calculating parking duration without manual intervention.

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Smart Parking Billing and Multi-Payment Integration

• Combines AI recognition hosts with backend operation systems to support multiple payment methods (cash, credit card, electronic payment, and third-party payment), automating parking billing and real-time accounting data integration.

Multi-Site Entrance Integration and Traffic Management

• Industrial-grade AI video recognition hosts can be deployed at multiple entrance nodes to centrally manage vehicle entry/exit records, parking status, and abnormal events for large campuses, hospitals, malls, and smart-city public facilities.

Automatic Vehicle Recognition

• Uses license plate recognition or eTag for vehicle entry/exit identification and access control across member vehicles, monthly rental vehicles, and temporary parking vehicles.

Community Automatic Vehicle Recognition Management

• For residential and commercial building surface or underground parking lots, the system can be built or leased to upgrade access without remote controls, automate lane recognition, and support parking committees in temporary parking billing and operational planning.

Occupancy Detection and Recognition

• Outdoor parking areas can integrate PTZ cameras to automatically recognize vehicles and license plates, while indoor underground parking can detect stall occupancy and plate numbers, with parking guidance display and search.

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Basement Parking Lane License Plate Recognition System

• For underground parking lane entry and exit, the ANPR system can detect front and rear plates and automatically capture related images to improve recognition rate and accuracy.

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Campus Parking Lot Entrance and Exit

• Network cameras installed at car and motorcycle lanes use intelligent license plate recognition for vehicle identification. The system supports active recognition of different vehicle types, including cars, motorcycles, heavy motorcycles, EVs, gravel trucks, large trucks, and special vehicle plates.

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Community Entrance/Exit License Plate Recognition 1

• Uses license plate recognition to manage parking permissions and confirm whether a stall is occupied by the authorized owner’s vehicle. Supports RFID reader integration, LED display control, and vehicle matching.

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Community Entrance/Exit License Plate Recognition 2

• Manages parking permissions and verifies whether the vehicle belongs to the corresponding stall owner. Supports RFID reader integration, LED display control, vehicle matching, and automatic gate opening.

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Headquarters Building Parking Management System

• Manages vehicle entry/exit and parking guidance with license plate recognition at entrances and exits, verifying resident vehicles and occupancy status; occupied stalls display red, while available stalls display green.

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Outdoor Parking Lot Entrance and Exit

• Factory entry and exit are managed by ANPR, allowing guards to monitor vehicles in real time while transmitting recognition results to the central system.

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Guard Room Onsite License Plate Recognition Monitoring System

• Community entry and exit are managed by license plate recognition to verify resident vehicles. Supports RFID reader linkage, LED display control, and automatic lane gate opening after matching.

Community Automatic Vehicle Recognition Management

• Replaces remote controls or access cards with license plate recognition, enabling convenient resident vehicle access and fully automated operation to reduce labor costs.

Smart Parking Lot Vehicle Positioning Control

• Provides parking automation and multi-screen guidance with real-time stall recognition and occupancy management, vehicle identification for each stall, and abnormal event notifications.

Vehicle Center Management System (VCMS)

• Uses license plate recognition to manage parking permissions and verify whether a stall is occupied by the authorized owner’s vehicle. Supports simultaneous multi-lane plate recognition.

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Technology Enforcement for Channelized-Line Crossing Violations

• High-resolution cameras can detect vehicles and recognize plates across wide lanes to support technology enforcement for channelized-line crossing violations.

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Mobile In-Vehicle License Plate Recognition

• Uses dashcams or network cameras with 4G transmission and GPS positioning to integrate data with map platforms for real-time recognition and location.

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Real-Time Intersection License Plate Recognition

• The AVI system records vehicle travel time and routes for traffic data collection and management between intersections.

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Real-Time Intersection License Plate Recognition

• The AVI system records vehicle travel time and routes for traffic data collection and management between intersections.

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Real-Time Intersection License Plate Recognition

• >The AVI system can use urban intersection license plate recognition or RFID reading to build travel time statistics for road evaluation, recording vehicle travel time and routes for traffic data management between intersections.

Synchronized Multi-Vehicle Plate Recognition

• Effectively identifies vehicle plates entering specific intersections and synchronously processes recognition results when multiple vehicles pass at the same time.

Mobile Vehicle-Mounted License Plate Recognition

• Uses dashcams or network cameras with 4G transmission and GPS positioning to integrate data with map platforms for real-time recognition and location.

Vehicle Travel Time Statistical Analysis

• Integrates fixed network camera license plate recognition data to calculate vehicle passage time, applying normal distribution statistical analysis and confidence intervals as segment travel time estimates.