Architecture and Engineering

general specification for video analytics solution the proposed video analysis solution should meet all the requirements detailed in this a\&e specification document supported cameras onvif/rtsp the solution should support analysis of any onvif / rtsp video streams from fixed, angled, or overhead cameras these could be ip cameras or analog cameras via encoders ccd cameras the solution should support the analysis of video streams of optical cameras the solution should support a minimum resolution of 480p and should be able to support higher resolution streams (i e 720p / 1080p) to improve detection distance and accuracy the maximum supported resolution should be 4k the solution should support the analysis of video streams with a minimum frame rate of 8 fps thermal cameras the solution should support the analysis of video streams of thermal cameras the solution should support a minimum resolution of qvga (320 x 240 pixels) and should be able to support higher resolution streams to improve detection distance and accuracy the solution should support the analysis of video streams with a minimum frame rate of 8 fps video analysis technology the solution should be based on deep learning technology for target detection and classification target types the solution should support the automatic detection and classification of the following target types person standing, fallen, or lying on the ground rule based event detection & analysis capabilities analytics rules the solution should offer a suite of analytic rules to provide real time detection of the following behaviors target/s moving in an area/loitering target is moving in the region of interest for a user defined duration target/s crossing a line – the target has crossed a user defined line in a specific direction or any direction slip & fall detection – a person is falling on the ground and detected lying on the floor each detection rule should apply to the relevant target types the user should be able to select several relevant target types for each detection rule the solution should be able to detect the existence or disappearance of custom objects in/from a user defined region of interest rule configuration and setup the solution should provide the ability to execute bulk operations for activating, deactivating, and scheduling multiple analytics rules the solution should enable any combination of analytics rules to run on the same camera simultaneously, without limitations the solution should enable the operator to define multiple detection regions per camera scene calibration each camera should support automatic and manual calibration calibration refers to the translation from pixels in the image to actual size (meters/feet) in different parts of the image the system should automatically calibrate object sizes in the image based on standard sizes of dnn classified targets in the scene, over time the system should have the possibility to override the automatic calibration or calibrate scenes where no movement occurs manual calibration should support different pixel to meter translations for different parts of the image, creating a flexible calibration mesh across the image frame all calibration methods should support complex translations creating accurate translations also in challenging environments resulting from e g fish eye/distorted camera images and scenes with multiple levels event generation the solution should provide real time generation of events to alert operators when a behavior is detected that matches the user defined rule the solution should support simultaneous tracking of multiple targets within the detection regions and/or the cross lines the solution should generate a short event video clip for each detected event, showing several seconds before and after the event, and include a bounding box around the target that triggered the event event integration the solution should be able to send the events to the following external systems milestone xprotect vms genetec security center vms immix cs and immix gf sentinel patriot mobotix mxhub and mxmanagementcenter other systems based on webhooks protocol (http push) other systems based on smtp protocol anonymization the solution should support static and dynamic video anonymization functionality the functionality should be individually configurable per video stream different anonymization methods should be supported standard pixelation application to grayscale images and streams standard pixelation application to color images and streams pixelation implementations each frame in the video data is permanently, destructively, and masked the original video data should not be possible to recover video investigation method of operation the system should analyze all cameras in real time and create metadata that will be stored in a database it should be possible to search any camera with a delay of no more than 10 seconds in real time the system hardware specification will facilitate the real time processing of all cameras it should be possible to search any or all cameras in the installation simultaneously and without needing to process the cameras in small batches, regardless of the number of cameras installed in the system geospatial awareness geospatial mapping the solution should enable the user to configure the following geospatial data per video source connected to the solution the video source’s location on a map the video source’s fov (field of view) registration – correlation of points in the fov with a map alternatively, the solution should retrieve the geospatial data from 3rd party systems geospatial analysis the solution should be able to present real time events or video investigation search results over a map the solution should allow a map based selection of relevant cameras within a user defined zone, for video investigation the solution should be able to present a tracked target path over a map system health monitoring the solution should self monitor its main components to ensure high availability and reliable video analysis this monitoring should include the following aspects ability to properly pull the onvif/rtsp video stream minimal video stream frame rate and resolution scene lighting (too dark/saturated / blocked) event delivery status monitoring of analytics servers (the computer running the video analytics) the solution should support configurable thresholds for detecting and generating health alerts the solution should be able to send health alerts to a configurable email recipients list system architecture the solution should be based on the following main components analytics server(s) the analytics server(s) pulls video streams from cameras and performs initial video analysis the analytics server(s) communicates with the core server(s) the analytics server(s) should support low bandwidth connection to the core services, down to 5 kbps per camera the analytics server(s) should be able to scale to support any number of cameras and should support both scale up and scale out resource scaling to increase performance a gpu should not be required the analytics server should support both cloud based, on premise, and hybrid deployments on premise installations without internet access (“fully offline”) should be supported real time event integrations to third party systems from the analytics server should be supported public api the system should expose a public, documented, api the api should require authentication and encrypted communication the api should support all functionality in the system, including management of cameras and analytics, management of events and health alerts, monitoring of system health and real time events, and the ability to create and get results from a video investigation