About JuliaEbling

This article describes which camera settings influence the performance of Essential or Intelligent Video Analytics in Bosch IP cameras, and what the best practices are. 📚Overview: 1. Resolution & Base Frame Rate 1.1 Video analytics frame rate dropping? 2. Scene modes & image enhancement 2.1 Brightness, contrast, saturation, white balance 2.2 Automatic level control (ALC): ALC level, shutter, day/night 2.3 Advanced: Contrast, noise reduction, backlight compensation 3. Privacy masks & display stamping 3.1 Privacy masks 3.2 Display stamping 4. Encoder settings 5. Thermal camera Which camera settings influence Video Analytics performance and why? In general there are three different processing blocks: Imaging, which reads out the camera sensor and generates the video images Encoder, which takes these images and prepares them for transmission over the network Video analytics In Bosch IP cameras, the following video analytics can be available: Intelligent Video Analytics (IVA) including IVA Pro, IVA Flow, Intelligent Tracking, Essential Video Analytics, and MOTION+. For simplicity, all those will be summarized as “video analytics” in this whitepaper wherever differentiation is not necessary. Those video analytics get the images to analyze directly from the imaging block and are completely independent of the encoder, which starts processing the images in parallel. Thus, all settings influencing imaging also influence video analytics, while the encoder settings are of no importance for video analytics performance. There are a view special cases like privacy masking, display stamping and thermal cameras, which will be covered here as well.   As the encoder sends out the video images as soon as possible, and video analytics are running in parallel, the video analytics results and metadata are typically send out 1-2 frames after the corresponding images. In live view, everything is displayed as soon as it is available to minimize the lag caused by encoding, transmission and decoding. Therefore no synchronization of video and metadata occurs, and the objects / changes detected by the video analytics seem to lag behind. When viewing recorded video, this is no longer visible as proper synchronization is done there.     Step-by-step guide   1. Resolution & Base Frame Rate The application variant of the camera and its basic settings determine the resolution of the video as well as the base frame rate. It is configured via Camera -> Configuration -> Installer Menu within the device’s web page, or via General -> Initialization within Bosch’s Configuration Manager. Please see the tech note on VCA Capabilities per Device to see what internal video analytics resolution and frame rate based on the video analytics type and settings chosen here are used.   Note that the frame rates selectable in the encoder settings depend on the base frame rate but are independent of the frame rate the video analytics uses. Furthermore, the video analytics results and metadata will be delivered with the frame rate used for the video analytics itself. Thus, it is for example possible that the encoder delivers the video with only 1 frame per second (fps), but the video analytics results and metadata are actually delivered at 15 fps. It is also possible that the encoder delivers 60 fps for the video, but the video analytics results and metadata are only delivered at 15 fps. This will also be visible in live view as well as when viewing recorded video, as the display of the video analytics results and metadata is updated as often as new information is there.   1.1 Video analytics frame rate dropping?   Dropping frame rates are of concern for video analytics. Fast movements especially close to the camera may not be detected anymore, and because the initial detection of objects will be delayed. The drop of single frames does typically not degrade performance significantly, but if the frame rate drops too much, then objects and alerts will be missed completely. Usually, the video analytics runs with 15fps or 12.5 fps, depending on the camera base frame rate, while MOTION+ only uses 5fps. For a complete overview on video analytics frame rates, see the whitepaper on „VCA Capabilities per Device”. There are special cases where video analytics frame rate may drop: Exposure time is set too large / gets too large. Then the imaging cannot provide the full frame rate to the video analytics. This can be avoided by ensuring that minimal frame rate in automatic exposure mode is not smaller than the specified video analytics frame rate. Further details can be found in section 3.1. There are too many objects are in the field of view to be tracked in real time by the video analytics. Then the video analytics will process the whole frame even if it takes longer, the delivery of the resulting metadata is delayed, and the video analytics drops the next frame or frames to proceeds with the next up-to-date image. Nevertheless, Intelligent Video Analytics / Essential Video Analytics will continue to track the objects. At the moment, this is how many object can be tracked in real time for the different common product platforms (CPPs) and video analytics: o IVA Pro Perimeter: ~20 o IVA Pro Buildings / Traffic: 64 o Intelligent Video Analytics on CPP6 and higher: ~20 o Intelligent Video Analytics on CPP4: ~10 o Essential Video Analytics: ~10 Overall processing load is too high and the video analytics does not get enough processing power. Depending on the common product platform (CPP) and architecture, the video analytics shares the processing power with the encoder. The overall processing load on the processor is then determined by whether and how many live streamings, recordings or device replays are taking place, and on the bitrates those use. In general, all have same priority and need to share processing power. (One exception is that JPEG streaming / snapshots have lower priority than video analytics, thus frame drops occur with the JPEG streaming.) o Intelligent Video Analytics on CPP4/6/7/7.3 uses a dedicated hardware accelerator unit for the full video analytics and is thus independent of the processing load on the encoder chip. o While CPP13/14 cameras have a dedicated neural network engine where object detection takes place with IVA Pro Buildings and IVA Pro Traffic, the tracking of the objects over time is on a processor shared with the encoder. Furthermore, the processor power inside the CPP14 cameras differs based on the camera model, with some models offering much less processing power per channel. o Essential Video Analytics runs on the processor shared with encoding. MOTION+ and tamper detection are negligible from a processing power point of view, independent of where they are processed.   2. Scene modes & image enhancement In general, an image pleasing and easy to interpret for a human is also an image that will provide a good video analytics performance. Video analytics don’t cope well with high noise levels, low contrast or blurred objects, and cannot detect anything not visible in the image, e.g. because the whole scene is too dark. Therefore artificial lighting or a thermal sensor are always advisable for use of video analytics at night. The available scene modes preset values which can then later be adjusted by the user. Instead of rating the user modes, the parameters they influence will be evaluated and described regarding their effect on video analytics.   2.1 Brightness, contrast, saturation, white balance   If the image is under- or overexposed, then details and contrast are reduced and video analytics performance drops. This effect can also be caused by setting extreme values of brightness, contrast, saturation or white balance. Therefore, best practice is to keep these values here at or near default, and white balance on auto.   2.2 Automatic level control (ALC): ALC level, shutter, day/night   The image brightness level is defined first of all by the amount of light reaching each pixel on the sensor. The exposure time, also called shutter time, defines the time span over which the light is aggregated. The larger the exposure time, the brighter the image will be. But a long exposure time also means that objects moving in the scene will become blurred, as they move over several pixels during the light aggregation time. On the other hand, the exposure time is restricted by the frame rate, as it can only aggregate the light for a single frame, and the next frame needs to be generated in time. To always generate images with a good contrast and brightness, the exposure time needs to be adjusted to the illumination levels outside. The brighter the scene, the shorter the exposure time can and should be, to avoid overexposure where the whole image becomes too bright and details and contrast are lost. The darker the scene, the longer the exposure time needs to be to avoid underexposure and dark images with details and contrast lost. Video analytics performance will drop along with the loss of details and contrast. Though some of the exposure effects are compensated in the image enhancements later on, e.g. using the gain intensifier, best practice is to generate the best possible image here as all later compensations can decrease image quality. Therefore it is recommended to use automatic exposure. To avoid frame rate dropping below the video analytics frame rate, the auto exposure can and should be restricted to a minimal frame equaling that of the video analytics used. This is important as exposure time can drop the frame rate down to 1 fps and below, causing objects and alerts to be missed completely with the video analytics. In addition, a default shutter time can be set. This default shutter time is used as long as possible, though in extremely bright or dark scenes the automatic exposure will have to adjust it. Best practice is to use ~ 1/30. Best practice is to keep the ALC level and saturation configurable there at zero. During low-light situations e.g. at night, color information is no longer available, and to reduce noise level, switching to a grayscale image is recommended. Best practice is to keep the day / night switch on auto.   2.3 Advanced: Contrast, noise reduction, backlight compensation   A high sharpness level leads to exaggerated and thus better visible edges. Though this is visually often pleasing, this feature can increase image noise, which may become a problem especially at night or in low-light situations. It can also add other small image artefacts to which the optical flow reacts sensitive. Best practice is to keep it at the default setting of zero. The aim of contrast enhancement is to provide the right amount of black and white in the image, and to boost the contrast of intensity values that occur often. Thus, low-contrast as well as camouflaged objects will be better visible to user and video analytics. Therefore, contrast enhancement should be active for best video analytics performance. Temporal noise filtering smoothest each single pixel with the values occurring over several frames. While temporal noise filtering provides smooth images even in low light situations, the temporal smoothing done to achieve this can cause moving objects to blur, creating ghosting effects. Though feedback from video analytics can be used to suppress the temporal noise filter in areas of moving objects with Intelligent DNR, in extreme low-light situations the blurring can be so extensive that the video analytics is no longer able to detect the objects at all. Therefore, best practice is to keep temporal noise filtering on a low level and Intelligent DNR off for best video analytics performance, and add sufficient external illumination instead. Spatial noise filtering smoothest each pixel with the values of the neighboring region in the same frame and can be used to reduce image noise. As it also decreases contrast and blurs objects, it may decrease video analytics performance. Best practice is to keep it on the default value of zero. Note that sharpness level, spatial noise filtering and temporal noise filtering have a value range from -15 to 15, where zero denotes the default value, which has been optimized for best overall visual performance. The actual “off” value can differ from camera to camera, and the lowest values may actually cause an inverted effect. Intelligent defog should be kept on auto, as it improves image quality in low-contrast scenes. The purpose of backlight compensation is to boost dark parts to brighter illumination values while minimizing their noise level, to generate good brightness and contrast levels in all parts of the image. However, bright areas thus get less contrast. Best practice is to use it whenever the scene has backlight, and keep it off otherwise.   3. Privacy masks & display stamping   3.1 Privacy masks On most Bosch IP cameras, privacy masking is applied to the image before it is given to the video analytics, and no video analytics will occur in these regions. Note that when the camera is shaking, false detections may occur at the border to the privacy masks as the privacy masks itself will stay stable and thus generate a different motion pattern there. On AUTODOME IP and MIC IP cameras based on the common product platform (CPP) 4, the privacy masking is applied to the image after being send to the video analytics. There, the video analytics also gets the information about the privacy masks to suppress processing there, but the user can actually choose to let the video analytics ignore the privacy masks to get alarms and metadata there. This is due to a different architecture on these devices, and cannot be transferred as a feature to the other cameras.   3.2 Display stamping Display stamping, like camera name, current time or logo overlays, is done after the video analytics get the image and therefore does not influence video analytics performance. Nevertheless, as the video analytics will work behind the display stamping but the user cannot see behind it to confirm video analytics detection there, best practice is to disable video analytics in the areas covered by the display stamping to avoid questions. This disabling can be done via VCA masks.   4. Encoder settings Bosch video analytics run in the camera and obtain the video images before they are passed to the encoder for compression and transmission over the network. Thus, all settings here are independent of and do not influence video analytic performance, up to and including frame rate settings (see section 1). The only exception is Essential Video Analytics, as this has no hardware accelerator and thus needs to share processing power directly with the encoding. Here, high bitrates and additional loads by live streaming, recording and device replay may lead to dropping frame rates of Essential Video Analytics. JPEG streaming / snapshots have lower priority than Essential Video Analytics, thus frame drops occur with the JPEG streaming instead of the Essential Video Analytics metadata.   5. Thermal camera In contrast to visible light cameras, the thermal cameras from Bosch (VOT 320, DINION IP thermal 8000) do not use the same image that is presented to the user, but are given a differently generated image with a dedicated contrast enhancement as well as tone mapping for optimal video analytics performance. Therefore, neither contrast enhancement nor tone mapping will influence video analytics performance on the thermal cameras.   ... View more

This article describes idle and removed object detection with Intelligent or Essential Video Analytics in FW 6.30, how to configure it and what the limitations are.   Applications: - Detect parked cars in no-parking zones - Detect blocked emergency exits - Detect the removal of museum exhibits - Detect suspiciously left packages in high-security areas - Detect idle bags in walk-through areas Limitations: - Not available on thermal cameras - Does not work in crowds - No abandoned baggage detection - No differentiation between baggage and persons sitting or lying down - Does not work in front of moving background like wind in trees or water - Does not work during illumination changes or camera brightness adaptations   Step-by-step guide   Abandoned baggage myths and facts: “Abandoned baggage”: A bag that has been carried by a person and has been placed on the ground (“idle”). Afterwards, the person left the bag and the surrounding area (“abandoned”). Application target areas are often crowded train station platforms or equally crowded airport waiting areas. THIS IS NOT POSSIBLE AT ALL: - The bags are often fully occluded by the crowd of people and thus not visible anymore. Something that is not visible cannot be detected. - The people are waiting, standing or sitting, and not moving any more than the bags do. Current idle object detection technology cannot differentiate whether a person or a bag is currently idle. Thus it will alarm on any waiting person as well, with a huge amount of false alerts. - Separating and tracking people in crowds is not possible with current technologies, thus the owner of an idle bag cannot be determined, and neither can be determined whether the owner left the premises and the idle bag is therefore really abandoned. - People wait at train station platforms and airports for hours at a time, much longer than any abandoned baggage should be alarmed on. - If people arrive in groups, like a family traveling, often someone is asked to look after baggage while the other leaves to buy something. Thus even while the correct owner left, the baggage is not abandoned at all, but well cared for. - The probability that a bomb is placed in a trash bin or hidden somewhere else is very high. No idle object detector will be able to find them then. - Even if an abandoned bag is detected, the time for a potential bomb to explode is too short to do anything about it. Two different solutions for idle / removed object detection: - Static background and stable illumination: Use reference image check from tamper detection. See the tech note on tamper detection for full details and configuration. - Changing illumination or background: Use idle / removed object detection tasks.   Different kinds of idle / removed objects: Technology Idle and removed object detection is always based on the comparison of a current image to a background representation, which can be a single static background like with the reference image check from the tamper detection, or a learned background image as with Intelligent or Essential Video Analytics. Thus it also has all of the limitations from background subtraction algorithms like not being able to separate close objects as they only appear as a single foreground blob and being confused by illumination changes and motion in the background as those are changes to the background, too. For best performance, use Intelligent Video Analytics on CPP6 or higher, as this has the most advanced object reasoning using other features like optical flow, trajectory analysis and object shape analysis, as well as the best background maintenance and idle object detection based not only on one background image but a statistical representation over time. The difference between an idle and a removed object for the video analytics is basically whether edges were taken away or added into the image. Started and stopped objects are tracked in addition, allowing further evaluations. For learned background images, idle and removed object detection is essential for background image maintenance. Note that overall illumination changes or camera brightness adaptations fully disrupt the video analytics, as the difference between background image and current image becomes too large. While Intelligent Video Analytics on CPP6 or higher with FW ≥ 6.10 still continues to track the moving objects, all idle / removed object observations are still disrupted and stopped. All other versions loose the moving objects as well. Therefore while in theory quite long idle / removed time to alarm can be supported, in practice the idle and removed time to alarm should be as short as possible to avoid illumination change disruptions.   Configuration For the configuration of idle / removed object detection using reference image check please see the tech note on tamper detection. All else is described here. The basic metadata configuration options of idle / removed object detection are the time to alarm. The same time is also used to determine when to put idle objects into the background image and remove removed objects from the background image. Note that started objects have no configurable time to alarm, as the alarm is send as soon as the algorithm verified it is a started object. Other configuration options allow to fully enable or disable the metadata generation of idle / removed objects. For Intelligent Video Analytics on CPP6 and higher from FW 6.10 onwards, a separate idle object size for placed and taken objects can be configured. For all other cases, the minimal and maximal object sizes from Metadata Generation -> Tracking applies. Note that Metadata Generation -> VCA masks also suppress any idle or removed objects. Note that in front of moving background, the started, placed and taken object detection will not work. Intelligent Video Analytics on CPP6 and higher with FW ≥ 6.10 fully suppresses all started, placed and taken objects in front of detected moving background areas when Metadata Generation -> Tracking -> Noise suppression is enabled, including in Metadata Generation -> Tracking -> Ship tracking mode. In all other cases, expect a multitude of false idle and removed alarms in front of moving background. After configuring the metadata generation, an idle or removed object task has to be configured in the alarm rules. There, the alarm generation can also be restricted to a specified alarm area. As the time to alarm needs to be configured already for the metadata configuration, it is only added for information in the alarm rule wizards. ... View more

This article describes the camera tamper detection options available with FW 6.30: Scene too dark / bright, global change, reference image check. Applications: - Detect camera blinding - Detect camera being moved away from of its view - Detect the loss of sufficient overall illumination - Detect idle / removed objects, e.g. objects in front of an emergency exit Limitations: - Not available on thermal cameras   Step-by-step guide   1. Scene too dark / bright Application: Detect the loss of sufficient overall illumination. How it works: Scene too dark / bright evaluates the amount of incoming light at the sensor and generates a corresponding brightness level with values between 0 and 255. This is then compared to a user-configured threshold. The check is run every 10 seconds and needs to be true twice to alarm, thus the alarm is generated only if and after the event occurs for at least 20-29 seconds. The current brightness level is shown to aid in setting the threshold. Limitations: - The computed brightness value depends on the chosen camera / lens combination. Therefore the threshold may need to be adapted for every camera combination as well. - Not all cases of camera blinding can be detected with scene too dark / too bright. Even with covering the camera, the brightness at the sensor may still be quite high, and the brightness of artificial lights like torchlights is less than a very bright sunny day. Use reference image check to detect camera blinding instead. - Available since FW 6.30. (Prior to FW 6.30, the average brightness of the final image pixel was evaluated, however, as can be seen in the example below, current camera image quality is good enough to generate a properly bright image even from very dark or very blinded scenes, so the old alarming method was not reliable anymore) Some camera blinding situations and their corresponding brightness values. When the material covering the camera is not fully opaque or fully covering, brightness levels can be quite high. Torchlights and laser pointer were held very close to the camera to archive highest brightness possible, otherwise brightness values will be even lower.   2. Global change Application: Significant but short-term changes in the image How it works: 1. MOTION+ is running: MOTION+ signals the percentage of changed blocks in comparison to a 1 second old image. If this number is larger than the sensitivity threshold, then an alarm is immediately generated. Only the sensitive area from MOTION+ is evaluated. 2. Essential Video Analytics (CPP7) or Intelligent Video Analytics (CPP4) is running: The global change is determined by first evaluating whether too much foreground is detected, or secondly whether a comparison to the 1 sec older image is smaller than a threshold influenced by the sensitivity parameter. If this is achieved for 2 subsequent frames, then the alarm is generated. Only the sensitive area from Essential Video Analytics or Intelligent Video Analytics is evaluated. 3. Intelligent Video Analytics (CPP6/7) is running: Intelligent Video Analytics detects a significant illumination change, scene change or camera motion. Neither sensitive area nor sensitivity parameter have any influence. Limitations: - The alarm can also be set off by: o Illumination changes o Crowds of people walking through. o A person walking close to the camera and thus obstructing the camera view - Long-term stability of the uncompromised camera field of view cannot be evaluated - It cannot be distinguished which kind of camera tampering occurred - The alarm will reset as soon as the new scene is stable   3. Reference check Applications: - Detect camera blinding - Detect camera being moved away from of its view - Detect the loss of sufficient overall illumination - Detect idle / removed objects, e.g. objects in front of an emergency exit How it works: - A fixed reference image needs to be set. This reference image is then compared to the current image every 10 sec. For independence to illumination changes, only the edges in both images are evaluated. The evaluation can and should be restricted to significant and stable edges like those from buildings in the image via masking all other areas. As long as these edges are visible, then the field of view is unobstructed, the illumination level good enough, and the camera still on the desired field of view. To avoid false alerts due to short-term changes, both timer and sensitivity can be adapted. - To use the reference check for removed object detection, e.g. a painting on the wall: Restrict the evaluation to the object plus a small border around it. Adapt the sensitivity until every removal of the object raises the alarm. - To use the reference check for idle object detection, e.g. a box before an emergency exit, target the camera view on the floor or restrict the evaluation accordingly. Now select “appearing edges” for the “edge check”, and adapt the sensitivity until the camera alarms on your test objects. - The alarm will consist until the scene is again similar enough to the reference image - Performance may be improved by scheduling two different reference image check profiles for day and night. Limitations: - It cannot be distinguished which kind of camera tampering occurred - The reference check needs edges in the image which stay consistent during all hours and seasons to work properly A scene changing over day, night, and seasons The reference check for tamper detection should be focused on edges that are always visible (marked in red) to obtain best performance. Using the reference check for idle object detection: Focusing the camera view on the floor, setting the reference without any objects included, setting check to “Appearing edges” and increasing sensitivity until the counter “Seconds to alarm” starts to drop from the trigger delay time. As the trigger delay is quite large, here with 10 minutes, objects moving through will not trigger the reference check, only objects staying in the scene for longer than trigger delay. Using the reference check for removed object detection: Focusing the camera view on the object to be secured, setting the reference including this object, setting check to “Disappearing edges”. When the object is removed, the alarm will sound. Here with very short trigger delay for testing purposes only to immediately get the alarm state, increase trigger time for more robustness in practice. ... View more

Ship Tracking FW 6.30 This article describes how to track ships using Intelligent Video Analytics, how to configure this and what the limitations are. Applications - Harbour & canal surveillance - Perimeter protection on coastlines     Step-by-step guide   Common product platform (CPP) Bosch cameras can be clustered by their common product platform. As different platforms offer a different amount of processing power, this can make differences in the video content analysis (VCA) performance. For an overview of the different product platforms and the cameras belonging to them, see the tech note on VCA Capabilities per Device.   Limitations - Only available with Intelligent Video Analytics on Bosch cameras of CPP6 &CPP7 with FW 6.10 or higher - The camera needs to be mounted at the shore in a fixed position - It is not possible to track ships or other objects from a camera mounted on a moving boat, as the whole shore would be detected as moving objects. - No detection of swimmers possible . Of swimmers, usually only the head is visible, which is mostly smaller than the surrounding waves and thus too small to be detected - Detection of ships is faster and more reliable o the faster the ships are o the larger the ships are o the more the ships appearance differs from the water o the calmer the water is - Detection / tracking in water gates will not be possible if the water flushes through them so fast and turbulent that extensive foam formation occurs Configuration If the water body to be monitored is calm with small waves only, the default settings of Intelligent Video Analytics on the listed cameras will already work quite well. However, in windy environments and at coasts, the waves and white crests need to be compensated for, and the ship needs to be well separated from its bow wave, in order to minimize false alerts while still correctly detecting the ships. For this, the tracking mode “ Ship tracking” needs to be enabled. It can be found under “Metadata Generation” --> “Tracking”. As soon as “Ship tracking” is enabled, “Noise suppression” is automatically enabled as well and becomes non configurable. Note that “Ship tracking” is the tracking mode with maximal noise suppression, and thus also the least sensitive. Therefore it should not be used for surveillance on land. The ship tracking mode itself does not benefit from calibration information. However, if you want to filter objects by real size and speed, or get the geolocation of the ships, you need to add a calibration.   Tracking slow ships on calm water is possible with default IVA settings on the listed cameras   In most real world conditions water is full of waves and the special “ship tracking" mode is needed        ... View more

The museum mode of Intelligent and Essential Video Analytics is an optimized tracking mode to detect whenever someone tries to reach towards and touch a valuable asset like a museum exhibit. It can also be used in any other application where approaching a sterile zone from a more crowded environment shall be detected.   Museum Mode FW 6.30: This article describes what the Intelligent Video Analytics and Essential Video Analytics tracking mode “museum mode” is, where to use it, how to configure and what the best practices are.   Step-by-step guide   What is “museum mode”? For perimeter protection, Intelligent Video Analytics and Essential Video Analytics detect and track objects. Alarms are typically generated for well-separated objects based on alarm fields or line crossings a line based on the object’s center. Sometimes, however, an alarm is needed as soon as any part of the object, like a hand, comes into the alarm area. Combine this with a sterile zone directly besides an area where lots of people move around, and then this is when you need the museum mode. The name is derived from one of the applications: Alarming whenever a museum visitor comes too close to an exhibit.   Typical „museum mode“ application: A sterile zone right in front and including the picture is next to an area where many people are expected. All areas outside the alarm field are masked out:   Applications - Protect paintings and exhibits - Protect barriers against climbing - Protect sterile zones right next to crowded places - … and whenever you wonder why a person reaching with a hand into an alarm field does not trigger the alarm Common product platform (CPP) Bosch cameras can be clustered by their common product platform. As different platforms offer a different amount of processing power, this can make differences in the video content analysis (VCA) performance. For an overview of the different product platforms and the cameras belonging to them, see the tech note on VCA Capabilities per Device.   Limitations General: - Geolocations of tracked objects not available - Difference in performance and configuration for Intelligent Video Analytics on CPP4 & Essential Video Analytics on the one hand and Intelligent Video Analytics on CPP6/7 on the other. Best performance available with Intelligent Video Analytics on CPP6/7. Intelligent Video Analytics: - Not available on the following Bosch IVA cameras with FW 6.10 – FW 6.20: o DINION IP starlight 8000 MP o DINION IP ultra 8000 MP o FLEXIDOME IP panoramic 7000 MP - For FW versions smaller than 6.10, configuration of the museum mode is available but hidden. - Idle / removed objects not available on CPP6/7 - Object color filter not available on CPP6/7 - Shaking camera will not be compensated for Configuration: Intelligent Video Analytics on CPP 6/7 with FW ≥ 6.30 (1) Set the alarm area for the sterile zone to be protected. (2) Mask out all other areas. (3) Decrease the “debounce time” of the alarm area to get the alarm as fast as possible (4) Set the tracking mode to “museum mode” (5) In case of false alerts, use sensitivity, noise suppression and min object size as filters Calibration is not required for or used by the museum mode. Configuration: Intelligent Video Analytics on CPP4 with FW ≥ 6.10 or Essential Analytics with FW ≥ 6.30 (1) Set the alarm area for the sterile zone to be protected. (2) Decrease the “debounce time” of the alarm area to get the alarm as fast as possible (3) Set the intersection trigger to “edge of box”. Thus every part of the object reaching into the alarm field will trigger the alarm. Note that false alarms may occur if the alarm field is sloped, as the object bounding box is used for the evaluation instead of the object shape. To display the object bounding box, right-click on the video image and select “Show”->”Object Bounding Boxes” (4) Set the tracking mode to “museum mode”. This means that the raw segmentation outline will be used in each frame. Though the raw segmentation looks very ragged, this is necessary as the hand of a person is very small in comparison to the person itself, and needs to be preserved in the object outline to correctly alarm on it. Furthermore, close objects will immediately be merged together, as any occlusion handling will be confused by too many objects anyway, and the shapes are estimated / smoothed during occlusion handling which again may cut the hand from the person outline. (5) Disable “noise suppression” for better sensitivity & faster detections. (6) Raise the stopped object debounce time so standing persons will not be regarded as idle and lost to the tracking. In addition to, or even replacing the activation of the museum tracking mode, the area outside the alarm field can be masked out. This reduces the needed processing power and neatly avoids the potential crowded situations outside the sterile zone. If objects reach into the alarm area and out again very fast, then is better to add some distance between the alarm field and the VCA mask to support earlier detection of the objects. Calibration of the camera is not required for or used by the museum mode.   Intelligent Video Analytics on CPP4 & Essential Video Analytics museum mode: Ragged segmentation outlines but preservation of the hand. Alarming on edge of bounding box:   The field intersection trigger “edge of box” means the field alarms whenever any part of the object’s bounding box overlaps with the alarm field:   The video menu where the display of the object bounding boxes can be enabled.   ... View more