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Intrusion Detection System

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Identifying intruders is primary to all security integrators and providers. False positives or false alarms result in losing confidence in your systems with alert or noise fatigue. Scylla can eliminate 99% of false alarms by defining the object of interest for monitoring and focus on the zone of intrusion. High performance solutions are required for effective security and Scylla’s Intrusion Detection & Perimeter Protection System (IDS) is the answer.

Scylla uses real-time artificial intelligence (AI) to analyze data collected from your security cameras. You are able to utilize our security dashboard to define the object or area of interest.

Data is collected from your cameras. Motion frames are received and are processed through our AI engine. Scylla reviews the frames and determines if an image contains a human or not. If the image also contains a visible face, Scylla can utilize facial recognition technology to identify the intruder.

Scylla includes an API that provides the ability to receive and analyze a batch of frames and respond with a detection status of “True” or “False” to reduce the number of false positives.

Scylla IDS includes a Social Distancing detection and alerting engine to monitor high-traffic areas. The social distancing engine will review and scan viewable areas to alert security personnel of occupants not adhering to social distancing policies.

  • Use Scylla IDS system for calculating people density
  • System for counting people in the region of interest
  • Real-time people counting and occupancy monitoring
  • Receive alerts if the maximum occupancy threshold is exceeded
  • There are configurable zones available of a camera for counting density
  • Fire alarm notification if a zone exceeds its density threshold
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FAQ

Scylla Intrusion Detection and Perimeter Protection System is one of the most flexible solutions as it can be easily integrated with most VMS and CCTV systems. Depending on the infrastructure the client already has on site, the integration can be implemented one- or two-way. In case of one-way integration, Scylla IDS uses video streams from existing cameras directly or through NVR/DVR they are connected to. The analysis output in this case is shown in Scylla's dashboard. In case of two-way integration (e.g. with major VMS providers, such as Mobotix, Genetec, NX Witness, etc), the output can be viewed in the corresponding VMS dashboard.
Typically less than a second. In cloud deployments the response time can slightly increase depending on the client’s upload speed.
Currently, the objects detected by Scylla IDS include humans and/or several types of vehicles (car, truck, bus, van, motorcycle).
Yes, it does. Moreover, almost all Scylla products can be deployed on the cloud. In the cloud-hosted scenario, the video/frames should be forwarded from CCTV cameras to the cloud in one of the following ways: 1) by using the proprietary Scylla Connector software that connects to the camera, takes the video stream, encodes it and sends packet-by-packet to the cloud, where it is further decoded and analysed. This architecture is favored where there are some restrictions (e.g. a domain, port, etc) in the local network. 2) by utilising the embedded algorithms of the camera (i.e motion detection) that send one or several frames, or a video chunk to the cloud through HTTP/FTP and similar protocols. This scenario is favored when the client is looking to minimize their upload bandwidth. 3) by exposing cameras to the cloud directly. This approach is the least popular due to related technical difficulties and security concerns.
Absolutely. Scylla does not store any data that can be considered personal. No footage or images are stored. The only cases when we store data is the reported alerts. Their retention times can be adjusted to comply with regulations the client has in place.
Scylla Intrusion Detection and Perimeter Protection System works pretty much the way human vision does - not only it sees an “odd” object in the scene, but it also classifies the origin of it. Scylla AI is trained on a huge and versatile dataset, therefore, it can effectively recognise humans or different vehicle types. The alarm is formed only if Scylla AI detects one of these objects or parts thereof.
An alert is classified as a true alarm when the prediction of AI corresponds with the reality (i.e. the object of interest is correctly identified, the action sought after is detected, etc.). A false positive is the case when the alert is triggered by mistake. Unfortunately, due to the essentially probabilistic nature of AI the latter are inevitable in most cases. However, due to the elaborate AI and machine learning behind Scylla IDS, it can meet any level of production-grade industrial standards. Moreover, we are continuously improving Scylla modules where they are retrained on mistakes to make sure the number of false alarms goes even further down with time.
Scylla IDS is essentially camera-agnostic. Most questions on the limitations and camera requirements end up receiving a simplified “rule-of-thumb” answer - if a human can see and identify the object of interest, then Scylla IDS AI will also be able to do that (and in some cases will even outperform due to the integrated zooming and re-checking algorithms). As for the minimal camera parameters, these will depend on your use case and the object of interest you are trying to detect. Of course, the camera should have a digital output or at least be connected to a DVR that has one. Scylla Intrusion Detection and Perimeter Protection System can accept pretty much all the variety of stream types, such as RTSP/RTMP, HTTP, etc. Usually the minimum required resolution starts from HD (1280x720) and 5 FPS. Parameters defining the frame/image quality vary from one camera to another, but we recommend looking into such characteristics as bandwidth, encoding, and sharpness, and improving them if necessary. Lastly, if Cloud+FTP implementation is desired, the camera should have a sensitive motion detection function with a possibility of upload to a FTP server of preferably more than one frame.
The duration of alerts depends on the client's data retention policy. By default we offer a storage duration of one month, but this period can be configured to correspond to local policies.
Yes, Scylla IDS easily analyzes video feeds from PTZ cameras. It can even be applied on UAV-mounted or body-worn cameras.
Scylla Intrusion Detection and Perimeter Protection System alerts the corresponding security unit/individual about the act of intrusion. The alert contains invaluable visual and meta- information about the origin of the event of intrusion. Moreover, the system can be connected to ACS (Access Control Systems) to lock infrastructures, alert and disable entry protocols, etc.
The limitations here highly depend on the camera specifications (see the Question 8). The most important factor is the resolution and view angle of the camera that eventually result in the pixel size of the object in the frame streamed by the camera. Similarly, factors such as illumination, capture contrast, video streaming parameters (bandwidth/encoding) are to be considered. As a rule of thumb, the distance can be defined as following: if a human can look at the footage and reliably tell that it’s the object of interest (human/vehicle) within the region of interest, then Scylla AI should also be able to do so. Scylla IDS installed on 4K drone cameras can detect humans at distances of up to ~250m. Alternatively, one can use the following information to define the limitations: the minimum height of a person on the frame should be ~25 pixels, and that’s 1.2% of 4K images.
The notification can reach the end user through one of the alerting pathways, namely: 1) Scylla’s dedicated web-based dashboard that shows alerts and allows to configure the system and monitor your environment, 2) Mobile alerting application, Client’s VMS dashboard (in case it is supported by Scylla IDS, e.g. Milestone, Genetec, NX Witness, etc.), 3) Access Control System notifications, i.e. signal lights and sounds attached to alerting relay boards.
Scylla IDS is designed to work in challenging environments where cameras with embedded algorithms misperform. The AI engine it runs on compensates for the drawbacks imposed by demanding conditions including but not limited to poor illumination, somewhat corrupted frames, environmental factors and weather-foisted effects.
Yes, it can, including border control distance IR/thermal cameras. The DRI parameters (Detection, Recognition and Identification) of Scylla IDS will depend on the camera characteristics (contrast ratio, pixel crosstalk, etc). But in general, the solution complies with industry standard DRI requirements, i.e. the recognition limit (the distance at which you can determine an object’s class - is it a human or a car, a truck or a tank, etc) is ~15 pixels for humans and vehicles.
If Scylla Face Recognition Module is deployed together with Intrusion Detection and Perimeter Protection System, it can enable white- and blacklisting of individuals detected on site. However, as face recognition module is a standalone solution that is optional for Scylla IDS, a few points are to be considered: 1) To run Face Recognition Module you have to provide additional hardware. Face recognition requires additional ~2Gb GPU memory and some computing capacity. 2) Accuracy of Face Recognition Module in “watchlist implementation” highly depends on a number of external factors, such as the visibility of the face of the individual, the size, the angle of it, possible obscurations, etc. While Intrusion Detection can be triggered when even a small part of a body is in the active area, Face Recognition is more demanding. To boost accuracy of Face Recognition in that respect Scylla IDS needs to be implemented together with Person Tracking to increase the chances of identification in cases when the individual’s biometrics are visible. However, Person Tracking is a resource demanding algorithm that will require additional hardware. 3) As mentioned above - Face Recognition Module is a separate product that needs to be purchased separately.

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