Image analysis function based on Deep Learning, allowing to recognize the faces of people in the detection zone. The camera, thanks to the appropriate algorithms and the built-in face database, is able to compare the recognized face with the database and trigger a programmed reaction.
Image analysis function based on Deep Learning, enabling the graphical representation of the intensity of traffic in the observed area. The algorithm of the function creates an illustrative map of the intensity of the movement, overlaying the image from the camera with a color palette, in which warmer colors correspond to greater intensity of movement, and cooler ones - to a lower intensity.
Image analysis function based on Deep Learning, allowing to detect and analyze the number of people waiting in the queue. The camera can trigger an alarm when the queue length exceeds a certain number of people, or when the service time exceeds a certain time.
Image analysis function based on Deep Learning, allowing the detection of a certain level of crowd density in a defined area. The camera can trigger an alarm when the number of people in the protected area exceeds the defined number.
Image analysis function based on Deep Learning, allows to create a virtual detection zone and trigger an alarm after violating the boundaries of this zone by defined objects with recognized shapes (a human or a car).
- perimeter protection of the facility
- protection of strategically important objects
Image analysis function based on Deep Learning, enabling the counting of objects with recognized shapes (a human, a car), that crossed the virtual line. The camera can display the number of these objects in the image.
Intelligent image analysis function based on Deep Learning, enabling the recognition of human and car shapes. The sophistication and efficiency of the analysis methods used, allow for distinguishing objects even as small as a few percent of the scene, and for simultaneous monitoring of many objects of different types.
Image analysis function based on Deep Learning, allows to create a virtual detection line. If the line is exceeded by defined objects with recognized shapes (a human or a car), an alarm is triggered.
- perimeter protection of the facility
Two way power function allows for using DC connector in camera (12V) as an output, when camera is powered by PoE. Maximum power output is 3W, so this solution is recommended mostly for active powered microphones (for cameras with audio input).
The recorders in combination with the cameras of the same series, apart from the configuration of basic parameters such as contrast or brightness, allow for a much more advanced interference with the camera settings. Depending on the functionality of the camera, from the recorder level it is possible, among others:
- changing the network settings of the camera
- stream parameters setting
- setting the zoom and focus in a camera with a motor zoom lens
- enabling and configuring the image analysis function
Lens with the possibility of remote focal and focus adjustment using a recorder, web browser, NMS software or even a mobile application.
Improved coding standard allowing even more efficient use of the transmission bandwidth. The mechanism is based on the use of frame elements virtualization, which allows for the partial prediction of the recorded image, thus the overall packet traffic in the network is reduced.
5 MPX CMOS sensor 1/2.7” OmniVision
|Number of Effective Pixels|
2592 (H) x 1944 (V)
0.03 lx/F1.2 - color mode,
0 lx (IR on) - B/W mode
auto/manual: 1/5 s ~ 1/20000 s
|Digital Slow Shutter (DSS)|
up to 1/5 s
|Wide Dynamic Range (WDR)|
yes (double scan sensor), 120dB
|Digital Noise Reduction (DNR)|
|Defog Function (F-DNR)|
|Highlight Compensation (HLC)|
|Reduction of image flicker (Antiflicker)|
motorized, f=2.7 ~ 13.5 mm/F1.8
zoom trigger, manual trigger
mechanical IR cut filter
auto, manual, time
1 ~ 36 s
|Visible Light Sensor|
2592 x 1944, 2592 x 1520, 2304 x 1296, 2048 x 1536 (QXGA), 1920 x 1080 (Full HD), 1280 x 960, 1280 x 720 (HD), 640 x 480 (VGA), 320 x 240 (QVGA)
30 fps for each resolution
H.264, H.264+, H.265, H.265+/G.711
|Number of Simultaneous Connections|
60 Mb/s in total
|Network Protocols Support|
HTTP, TCP/IP, IPv4, HTTPS, FTP, DHCP, DNS, DDNS, NTP, RTSP, RTP, UPnP, SNMP, SMTP, P2P, HTML5
|ONVIF Protocol Support|
from Internet Explorer, Firefox, Chrome, Opera, Safari browser
languages: Polish, English, Russian, and others
RxCamView (iPhone, Android)
4 video mask type: single color
|Video Content Analysis (VCA)|
abandoned object, object disappearance, line cross, zone entrance, zone exit, multi loiter, cross counting, heat map, queue length detection, objects distinguishing, face recognition, comparing faces
180˚ image rotation, vertical flip, horizontal flip
up to 5 s/up to 30 s
|System Reaction to Alarm Events|
e-mail with attachment, saving file on FTP server, saving file on SD card, alarm output activation, saving in the cloud storage
|Restoring default settings|
via web browser, using reset button
yes (hardware support)
1 x RCA/1 x RCA
1 (NO/NC)/1 relay type (max. 12VDC/300mA)
1 x Ethernet - RJ-45 interface, 10/100 Mbit/s
|Memory Card Slot|
microSD - capacity up to 256GB
with bracket: 87 (W) x 83 (H) x 242 (L)
|Degree of Protection|
IP 66 (details in the user’s manual)
aluminium, white, fully cable managed wall mount bracket in-set included
12 VDC, PoE (IEEE 802.3af, Class 3)
TVS 4000 V
5 W (IR illuminator on)
-30°C ~ 55°C
max. 95%, relative (non-condensing)
|Accessories with cameras compatibility_Kompatybilnosc akcesoriow i kamer_07.11.2022.xlsx||Compatibility table||
|NVR, NHDR with IP cameras compatibility_Kompatybilnosc rejestratorow i kamer IP_15.09.2022.xlsx||Compatibility table||
|IP2000_ 4000 cameras security recommendations_rekomendacje bezpieczenstwa_EN_PL_v1.0.pdf||Others||
|Image analysis - camera installation guidelines_wskazówki instalacji kamer_EN_PL_v1.0.pdf||User manual||
|Device Config Tool_126.96.36.199 with manual.zip||Software||
|Camera's maintenance tips_Wskazowki konserwacji kamer_v1.0.pdf||Others||
|PoE standards overview_Porównanie standardów PoE_EN_PL_v1.0.pdf||Others||