Page 107 - ETE Product Overview

Video Surveillance Product Catalog 2020
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Falcon
Human Recognition System
Features
High-performance, High-capacity
- Supports up to 500,000 cameras in one system
- Supports up to 10,000,000 Face / Human Detection Comparison Library - Real-time multiple face matching, matching speed low to 2s
- Up to 98% face recognition accuracy
Deep Analysis
- Supports deep learning algorithm
- Supports human attributes analysis
Good Compatibility
- Supports different types of camera: Kedacom human recognitive camera, Kedacom standard IP camera, Third-party IP camera
Abundant Functions and Features
- Three types of snapshot: face, head and shoulder, person
- Supports recognizing and capturing the human side and back picture - Supports searching picture by picture or attributes
Specifications
KProServer3 CPU Server
CPU Server Chassis
Computing Nodes
Functions
Operating Temperature Operating Humidity Power
Power Consumption Weight
Chassis Dimensions
Falcon Human Recognition System
High density server chassis
19" 2U standard blade rack server chassis, able to insert 4 computing nodes, each computing node configuration 6 slot of 3.5 inch / 2.5 inch compatible hard drive slot, a total of 24 hard disk slot can be placed
Include 2 unit of 2150W redundant platinum power supply
Computing Nodes for KProServer3-B01
2 x Intel Xeon Silver 4110 (8 core, 16 thread, up to 3.0GHz, 11MB Cache), 32GB RAM (KProServer3-N100-E01-32G) / 64GB RAM KProServer3-N100-E01- 64G) / 128GB RAM (KProServer3-N100-E02-128G)
1 x 2.4TB SAS, 1x 960GB SSD, 2 x SFP
OS license not included
Compares the attributes of each face
Calculates the similarity percentage between faces
10°C ~ 40°C / 50°F ~ 104°F
50% ~ 90%
100 ~ 127V AC, 50 ~ 60Hz / 200 ~ 240V AC, 50 ~ 60Hz
Max. 2150W (4 computing nodes included)
38kg / 83.78lb (including 4 x nodes)
733 x 438 x 86.9mm / 28.86’’ x 17.24’’ x 3.42’’
DeepEngine-1000 GPU Server
GPU Server Chassis
Computing Nodes
Functions
Operating Temperature Operating Humidity Power
Power Consumption Weight
High density server chassis
19" 4U standard blade rack server chassis, able to insert 6 computing nodes Include 4 unit of 1200W redundant platinum power supply
Computing Nodes for DeepEngine-1000
1 x 64 bit multi-core CPU, 2 x GPU, 32GB RAM
2 x 1000M RJ45, 240GB SSD
OS license not included
Identifies the faces and persons in the video streaming
Grabs the attributes of each face and person
0°C ~ 40°C / 32°F ~ 104°F
50% ~ 90%
100 ~ 127V AC, 50 ~ 60Hz / 200 ~ 240V AC, 50 ~ 60Hz
Max. 3000W (6 computing nodes included)
58kg / 127.87lb (including 6 x nodes)
820 x 440 x 177.8mm / 32.28’’ x 17.32’’ x 7’’
Windows10 Professional 64-bit / Windows 10 Enterprise 64-bit
Windows 7 Professional 64-bit / Windows 7 Enterprise 64-bit / Windows 7 Ultimate 64-bit
Elastic Search
Falcon Human Recognition System
Admin / User
Up to 500,000
Up to 10,000,000
Kedacom human recognitive camera Kedacom standard IPC (by Kedacom VMS) Third-party IPC (by Kedacom VMS and NVR) RTSP Video Stream (Optional)
Face and human pictures (By IPSAN)
Identifies the faces and persons in the video streaming
Person, Face, Full view
Real-time / Post-event
Analyzes attributes of human and face image, grabs the gender, age group, with or without glasses and mask, color of clothes, texture of clothes, direction, speed level
Date and time / Camera / Face image / Person image / Attributes (gender, age group, with or without glasses and mask, color of clothes, texture of clothes, direction, speed level)
Bi-directional
Face matching
Alarm report / Snapshot / Acoustic alarm
H.265 / H.264 1080p@30fps
TCP/IP, UDP, RTSP, VSIP, 802.1x, IPv4 EPAS Client
Chassis Dimensions
System
Supported Operating System
Database
Application System
User Management Maximum Number Of Camera / Video Stream Access
Face / Human Detection Comparison Library
Camera Access Type
Storage
Recognitive Functions
Target Detection Captured Picture Face Matching
Analysis
Search Mode
People Counting
Alarm
Alarm Triggers Alarm Events Video Compression
Live View Network Network Protocols User Interface
www.kedacom.com
Recognitive.Mobile.Integrated