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IVS-GS8000-GU2-HW EoL-L

Dahua Comprehensive Analysis Intelligent Server

> Adopts advanced face deep learning algorithms and gives full play to GPU parallel computing. With its algorithms, the device has enhanced recognition capabilities and performs exceptionally well, raising the bar in the industry.

> Tesla T4 intelligence analysis card with strong computing power.

> Performs intelligent analysis and compares tens of millions of pieces of massive data from faces, vehicles and structured services.

> Arms the blocklist database which has millions of pieces of face data.

> A type of rack mount server with multiple built-in slots and modular replacement that assist with changing the hardware.

> The system has cluster deployment and supports enhancing the performance of clusters when servers are added based on the video cloud distributed architecture.

> Supports algorithm storehouse.


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  • System

    Main Processor

    Two 16 C/32 T X86 processors, 2.1 GHz

    GPU

    Supports Four NVIDIA TESLA T4 GPUs (the GPU card is optional, and the following functions are available with the GPU card and relevant software permission).

    Operating System

    CentOS Linux release 7.4.1708 (Core)

    Memory

    Eight 16 GB DDR4 memory modules with up to 24 slots

    Disk

    Five 3.5"4 TB HDD which can be expanded to maximum 32 TB (each HDD is 4T) with up to 8 slots.
    7.2K RPM SATA 6 Gbps 512n 3.5"

    Face Analytics

    Face Detection

    Detects and analyzes face image streams by gender, age, expression, glasses, mustache, face mask, opened and closed eyes, and opened and closed mouths.

    Face Modeling

    Supports extracting attributes from face images.

    Face Arm

    Compares the face snapshot against a designated face database to get information on the first person that exceeds the arming threshold.

    History Alarm Records Query

    Quickly search for history records on arming alarms to review information on alarms that were previously triggered.

    Management of Registered Database

    Manage and use multiple types of databases such as the blocklist database, allowlist database, and static database. You can add, delete and modify these databases and their members.

    Search in Registered Database

    Quickly search for registered database members by name, gender, date of birth and ID card number.

    Search by Image in Registered Database

    Images can be used to search for information. The results are compared with data on humans in the registered database, and the matches are displayed based on their similarity level.

    Search in Snapshot Database

    Conveniently search for history records of passing persons in the snapshot database.

    Search by Image in Snapshot Database

    Images can be used to search for information, and the search results can be filtered by time and channel. The results are compared with data on humans in the snapshot database, and the matches are displayed based on their similarity level.

    1V1

    Supports 1V1 face comparison, and returns similarity result.;One T4 card supports up to 75 times/s (only in the case of working with video cloud platform, and exclusive use of hardware resources of single server)
    The actual performance depends on the software that the server works with.

    Cluster

    Supports clusters (when it is set to work with the video cloud platform)

    Image Stream Analysis Capability

    One T4 card supports up to 200 face images/s.
    The actual performance depends on the software that the server works with.

    Blocklist/Allowlist Database Arm

    When a single server perform arming with CPU hardware, it supports blocklist/allowlist database with 2 million data and can real-time alarm of 100 face features/s
    (The total number of databases in the video cloud system≤200, the total number of members≤2 million, the deployment threshold ≥80%, and exclusive use of hardware resources)
    The actual performance depends on the software that the server works with.

    Search by Image

    Up to 100 million face data (30 million in registered database and 70 million in snapshot database by default)
    Search by image and respond in seconds (only in the case of working with video cloud platform, and exclusive use of hardware resources of single server)
    The actual performance depends on the software that the server works with.

    Vehicle Analytics

    Vehicle Recognition Mode

    Recognizes the front and back of vehicles (supports recognition of license plates from Brazil, and configuring license plate recognition for other countries).

    License Plate Color Recognition

    White, black, yellow, blue, and green

    Vehicle Color Recognition

    Recognizes a variety of colors such as white, orange, pink, black, red, yellow, gray, blue, green, silver, brown and purple

    Vehicle Type Recognition

    Supports large bus, heavy truck, medium truck, sedan, van, truck, medium bus, SUV, MPV and pickup

    Vehicle Attributes Recognition

    Recognizes vehicle attributes such as tissue boxes, sunshields, pendants, perfume, cards and permits.

    Safe Driving Behavior Detection

    For drivers, it detects when they are not wearing their seatbelt, calling while driving and smoking.
    For co-drivers, it detects when they are not wearing their seatbelt and carrying a baby.

    Target Detection

    Supports vehicle recognition of incomplete snapshot

    Image Stream Analysis Capability

    One T4 card supports up to 3 million image/day (pixel from 2 MP to 9 MP)
    The actual performance depends on the software that the server works with.

    Vehicle Brand/Logo Recognition

    147 kinds

    License Plate Recognition

    License plate recognition with a minimum of 50 x 13 pixels is supported.
    The actual performance depends on the software that the server works with.

    License Plate Recognition Rate

    With normal light in the day and normal fill light at night, resolution ≥120 × 120, clear, distinguishable license plate.
    License plate accuracy: ≥96%.

    Vehicle Type Recognition Rate

    With normal light in the day and normal fill light at night, resolution ≥240 × 240, clear, distinguishable vehicles.
    Vehicle type accuracy: ≥92%.

    Vehicle Color Recognition Rate

    With normal light in the day and normal fill light at night, resolution ≥240 × 240, clear, distinguishable vehicles.
    Vehicle color accuracy: ≥95%.

    Safety Belts Recognition Rate

    ≥85% (vehicle glass is clear)
    The actual performance depends on the software that the server works with.

    Recognition Rate of Driver and Passenger Phone Calling

    ≥80% (vehicle glass is clear)
    The actual performance depends on the software that the server works with.

    Search by Vehicle Picture

    Images can be used to search for information, and the search results can be filtered by time and channel. The results are compared with data on motor vehicles in the database, and the matches are displayed based on their similarity level.

    Search by Vehicle Picture Performance

    Vehicle Search

    One server supports up to 40 million data in snapshot database, searching by image and responding in seconds(only in the case of working with video cloud platform, and exclusive use of hardware resources of single server)
    The actual performance depends on the software that the server works with.

    Metadata Analytics

    Target Classification

    Classify targets including face, body, motor vehicles and non-motor vehicles. Generate intelligent structured analysis on the live video, history video (when using with the platform), and the video files that were uploaded offline, and then extract structured information on the moving targets. (for face, only image can be viewed without structured analysis).

    Motor Vehicle Detection

    Detects motor vehicles by a wide range of attributes such as type, color, brand and plate number(supports recognition of license plates from Brazil, and configuring license plate recognition for other countries).
    Detects sunshield, not wearing seatbelt, calling, ornament (pendant and tissue box).

    Non-motor Vehicle Detection

    Detects pedestrians by a wide range of attributes such as gender, age, hair style, their top and bottom clothes and color, non-motor vehicle type and color wearing a hat, hat color, umbrella (canopy), umbrella (canopy) color, bag, bag color, shoe type and color, vest, mask color, raincoat, rearview mirror, truck, basket and direction.

    Pedestrian Detection

    Detects pedestrians by a wide range of attributes such as gender, age, hair style, their top and bottom clothes and color, wearing a hat, hat color, umbrella, umbrella color, bag, bag color, shoe type and color, vest, mask color, raincoat, cart, whether they are riding, and direction.

    Real-time Display

    Displays the analysis results in real time, and display humans, motor vehicles and non-motor vehicles in real time with tracking boxes.

    Search by Attribute

    Intelligent search of human, motor vehicles and non-motor vehicles by their attributes.

    Search by Image

    Images can be used to search for information, and the search results can be filtered by time and channel. The results are compared with data on humans, motor vehicles and non-motor vehicles in the database, and the matches are displayed based on their similarity level.

    Video Recording Task

    Generate intelligent structured analysis on history videos (when using with the platform), extract structured information on moving targets, and the video recording task can be accelerated automatically.

    Local Task

    Generate a structured analysis of your uploaded local video files, and extract information on moving targets, and the local task can be accelerated automatically.

    Display Tracks on the Map

    Yes (when using with the platform)

    Computing Node Cluster

    Supports clusters (when it is set to work with the video cloud platform)

    Metadata Analytics Performance

    Intelligent Video Stream

    Supports real-time display of structured intelligent video streaming, including target attributes, and displays up to 5 items at the same time. Pedestrians (age, gender, top clothes and color, hat, umbrella and bag), vehicle (model, brand, license plate and color)

    Metadata Analytics Performance

    One T4 card supports up to 32-channel real-time 1080p video analysis of moving target or 100 images/s (1080p) metadata analysis of target or 3 million images/day (1080p) structured analysis.
    The actual performance depends on the software that the server works with.

    Search by Image Performance

    One server supports up to 40 million data in snapshot database, searching by image and responding in seconds(only in the case of working with video cloud platform, and exclusive use of hardware resources of single server)
    The actual performance depends on the software that the server works with.

    Detection Rate of Moving Target

    With normal light in the day and normal fill light at night, resolution ≥40 × 80, clear, distinguishable pedestrians and non-motor vehicles:
    Detection rate of pedestrians: ≥95%;
    Detection rate of non-motor vehicles: ≥95%;
    With normal light in the day and normal fill light at night, resolution ≥120 × 120, clear, distinguishable motor vehicles:
    Detection rate of motor vehicles: ≥95%.
    The actual performance depends on the software that the server works with.

    Pedestrian Recognition Accuracy

    With normal light in the day and normal fill light at night, resolution ≥80×160, clear and distinguishable pedestrians:
    Gender accuracy: ≥90%;
    Hair style accuracy: ≥90%;
    Clothes style and color accuracy: ≥90%;
    Wearing accuracy: ≥90%;
    Belongings accuracy: ≥90%.
    The actual performance depends on the software that the server works with.

    Recognition Accuracy of Non-motor Vehicle

    With normal light in the day and normal fill light at night, resolution ≥80×160, clear and distinguishable non-motor vehicles:
    Gender accuracy: ≥90%;
    Hair style accuracy: ≥90%;
    Clothes style and color accuracy: ≥90%;
    Wearing accuracy: ≥90%;
    Belongings accuracy: ≥90%;
    Non-motor vehicle type accuracy: ≥90%.
    The actual performance depends on the software that the server works with.

    Vehicle Recognition Accuracy

    With normal light in the day and normal fill light at night, resolution ≥240×240, clear, distinguishable motor vehicles:
    Motor vehicle type accuracy: ≥92%;
    Motor vehicle color accuracy: ≥90%;
    Driver and passengers' safety belt accuracy: ≥85%;
    Driver and passengers' phone call accuracy: ≥80%;
    With normal light in the day and normal fill light at night, resolution width ≥120, clear, distinguishable number plate:
    plate accuracy: ≥96%;
    plate color accuracy: ≥90%.
    The actual performance depends on the software that the server works with.

    Port

    Network Port

    2 × 10000/1000 MB self-adaptive network ports

    USB

    2 × front USB3.0 ports and 3 × rear USB3.0 ports

    VGA

    2 × VGA ports

    Others

    1 × RJ-45 management network port

    General

    Power Supply

    100–127 V/200–240 V, 50/60 Hz, 10 A/5 A

    Power Redundancy

    Dual

    Power Consumption

    ≤ 800 W

    Operating Temperature

    10°C to 35°C (50°F to 95°F )

    Operating Humidity

    35%–80% (RH), maximum relative humidity is 90%(RH) (40°C ).

    Storage Temperature

    –40°C to 60°C (–40°F to 140°F)

    Storage Humidity

    20%–93% (RH)

    Gross Weight

    35 kg (77.16 lb)

    Net Weight

    27.5 kg (60.63 lb)

    Product Dimensions

    87.1 mm × 447.6 mm × 735.0 mm (3.43" × 17.62" × 28.94") (H × W × D)

    Packaging Dimensions

    273.0 mm × 754.0 mm × 1069.0 mm (10.74" × 29.68" × 42.09") (H × W × D)

    Installation

    Standard 19'' rack installation with guide rail

    BTU

    ≤2729.7 Btu/h

    Optional

    Product Type

    Hardware