|Place of Origin:||CHINA|
|Minimum Order Quantity:||1|
|Delivery Time:||10 days|
|Camera Extending Ability:||The System Support No Less Than 1000 Pcs Of Camera For Extending.||Vehicle Feature Identification:||System Support Real Time And Offline Behavior Analysis And Statistic For Vehicles Which Include Straight- Through Vehicles, Left Turn Vehicles And Right Turn Vehicles. Providing Data Reference For Future Traffic Light Optimization And Viaduct Construction.|
|GIS Map Function:||Support To Display Motion Trajectories From GIS Maps, Support Click To View Videos From GIS Maps||Traffic Violation Detection:||The System Can Monitor,collect And Summarize Real-time Or Offline Detection Of Existing Vehicles Under The Road, Vehicle Opposite Direction Driving Or Cross Line Events.|
|Long Time Stay In Specific Area:||The Vehicle Stay Over Certain Time In Specific Area, Then It Would Comes Alarm To End User. It Can Support Offline And Online Detection.||Human Traffic Statistics:||The System Supports Online And Offline Statistics On The Human Traffic In The Video Surveillance Specific Area. End User Can Get The Data Of Real-time Traffic And Historical Traffic For A Certain Period Of Time.|
AI, intelligent traffic, traffic, big data, structured video
With the booming trend of video surveillance cameras in last decade, high-definition networking video surveillance systems have been widely used for auxiliary management and anti-terrorism security over the world. In case of incidence, however, the monitoring process of the surveillance video is time-consuming and labor-intensive, whether it is event precaution, real-time monitoring in the matter, or security analysis in hindsight. Furthermore, large amount of video data are lost without effective information extraction, resulting in the inefficiency of the existing video surveillance systems.
Due to the urgent need of an automatic video analysis system, We have developed “GTVision”, which is a video content structuring and analysis system as intelligent as the human vision system. As the core system of government supervision, enterprise management and social security, it can extract the useful information during the whole surveillance process without losing any clue for event analysis. This system is an important information module for the infrastructure of smart city, smart transportation and smart security.
In the field of smart transportation, GTVision has advantage to manual monitoring as it is able to recognize and count motor vehicles, non-motor vehicles and pedestrians on all existing roads at an overall accuracy of above 90%, on a 24 hours by 7 days basis. By extracting the information from the video content of all roads and blocks, a group of census data can be acquired, such as traffic directions of all vehicle types, motion trajectory, and congestion level. All these data are beneficial to effective decision of a smart transportation system.
Fundamental research of the cutting-edge technologies in machine learning. Top level in the world.
Face recognition, Crowd density monitoring, Cross camera tracking, Vehicle flow monitoring, Traffic accident monitoring, Urban traffic scheduling.
Pedestrian, Vehicle, Ever increasing amount of data, It can display real-time traffic visualization and machine learning efficiency on LCD wall
Through the big data analysis, the system can provide traffic light management department with time plan suggestions and vehicle priority recommendations for traffic lights.
By setting up special monitoring camera and other equipment at traffic intersections, traffic data can be captured, and real-time traffic flow and speed data can be recorded through image analysis technology.
Traffic big data, can provide statistical analysis results for MMDA scientific adjustment of traffic lights, optimize road management, and provide scientific basis for the construction of viaducts and tunnels.
CPU: Intel(R) Xeon(R) CPU E5-2620 v4 @ 2.10GHz
GPU: GeForce GTX 1080Ti 11G
Minimum required resolution: 1280x720 (720p)
Precision rate: >90%
False positive rate: <10%
Maximum detected objects in a frame: 600
Minimum resolution of detected objects: 8*8 pixels
Deep learning network layers: ~200 layers
TPS by single GPU:
1080Ti: 75 fps, or 3 cameras (720P) full frame detection
P40: 100 fps, or 4 cameras (720P) full frame detection
P100: 125 fps, or 5 cameras (720P) full frame detection
Object Tracking across Cameras:
Accuracy under single camera: 90%
Accuracy across cameras : 80%
Simultaneous targets tracking number limit under single camera: 100
Minimum resolution of target: 8*8 pixels
TPS (throughput per second) by 4 GPUs: 300 frames (720p) per second or 12 cameras (720P) full frame detection
Top-1 accuracy rate: 80%
Top-10 accuracy rate: 90%
Single query response time upon million-level database: <100 milliseconds
Single query response time upon billion-level: <20 seconds
Intelligent Security: Real time video analysis Boundary monitoring Behavior analysis Multi-cameras Re-ID (Re-Identification)
Intelligent Operation: Staff flow analysis Ground service scheduling Vehicle scheduling
Intelligent Business: Passenger flow analysis Customer flow analysis Precision marketing
Intelligent Service: Fast locate passengers Fast locate luggage