Use this technical documentation to learn, apply and share about SWARM technology.

Who should read this?

This documentation is a technical guideline for partners and customers who are interested in integrating our software in a new or existing data analyzing and IoT environment.

Number plate area code (Early Availability Feature)

In addition to the number plate raw text and country code, we now support the number plate area code. Some number plates include an area code associated with a geographical area, such as "W" for Vienna (Austria) or "M" for Munich (Germany).

The following 13 countries are supported

• Austria

• Bulgaria

• Switzerland

• Czech Republic

• Germany

• Greece

• Croatia

• Ireland

• Norway

• Poland

• Romania

• Slovakia

• Slovenia

How does this work?

• For supported countries, we detect spaces between letters and parse the raw license plate text according to a predefined format.

• In the case of countries that are not supported (e.g. Italy), the generated event won't contain an area code.

How to configure?

All use cases based on ANPR are supported, no additional configuration is required.

How can I access the data?

The Data Analytics widgets "Journey Distributions" and "License Plates" allow segmenting by "License Plate Area"

Minor improvements and bug fixes

• Improved classification accuracy for the classes person and bicycle (Traffic & Parking Standard/Accuracy+)

• Fixed Data Analytics, some queries led to inconsistent data

• Fixed rule engine for Region of Interest occupancy, triggering a hardware output

• Fixed P100/OP100 slowdown introduced with 2023.3

• Reduced downtime during device updates

• Fixed debug video output pixelation of counting line overlays

• Fixed license notification for not enough SPS licenses

The SWARM Perception Box is a managed black box; meaning you do not need to manage hardware/OS/driver/security patches. You also cannot access the system.

To get set up, follow these steps:

1. Mount and Go Online

Your first step includes:

• Mount the (O)P101/P401 to the desired location.

• Ensure your (O)P101/P401 is online in your SWARM Control Center.

2. Device Configuration

After the Perception Box is successfully connected with the SWARM Control Center, you must configure the used camera and the scenario you are fulfilling.

3. Data Analytics

​

The SWARM Perception Platform consists of the following major components:

Data integration options

• Data Analytics API (high level)

  • Events are sent and stored in Azure Cloud environment managed by Swarm

  • Events are processed and stored by Swarm

  • The API enables easy integration with third party systems

• MQTT (low level)

  • Requires to operate an MQTT server

  • Requires the storing/processing of raw events

  • Enables on the edge processing for time-critical and/or offline use cases

Principles

The architecture is based on the following principles:

• Centralized data analytics, device configuration, and update management.

  • Hosted in the Microsoft Azure Cloud, maintained by Swarm.

• Decentralized camera stream processing at the edge

  • Generated data (Events) and management traffic are decoupled.

  • Support for heterogeneous network infrastructure

• Scale from one to thousands of SWARM Perception agents

P401 diagrams

Install NVIDIA drivers and the Docker engine

After you followed the installation guidelines you must be able to get a similar output

Install Azure IoTEdge

Configure Azure IotEdge

You will receive a ZIP file from Swarm with configuration files. (Replace $ID with the device ID you received from SWARM)

At this point check the IotEdge logs for any errors

You will now see your deployment in the SWARM Control Center as "Unnamed Device" with the registration ID:

At this stage, the IoT Edge runtime will pull the docker images and once finished the device can be configured in the Control Center.

Next steps: Configure your use case.

The Virtual Perception Box (VPX) is a software-only product that will be provided as docker containers (SPS license).

• You are fully responsible for the setup and maintenance of the hardware and software (OS, driver, networking, system applications, …)

• Production-ready checklist for devices with VPX (Swarm Analytics recommendation)

  • Integrate remote management (e.g. VPN/SSH)

  • Integrate update management (e.g. Ansible) to update system packages like IotEdge, Jetpack, Docker

  • Device monitoring

  • Check security (firewall, security patches applied, strong passwords/certificates)

Install JetPack 4.6.0

Free up hard disk space on your Jetson device (optional)

Install VPX Agent

With our installer script, installing the VPX agent is easy. Make sure to get the serial(s) from us in advance.

After the installation script is complete, the IoT Edge runtime will pull four docker containers as outlined below.

Make sure that the container curiosity-arm64-tensorrtis used.

You will see in the SWARM Control Center an "Unnamed Device" with the corresponding registration ID:

You want to know the traffic situation of an urban street or highway? SWARM software is providing the solution to get the number of vehicles passing at the street split per vehicle type (Classes) and direction.

What data can be generated?

For this use case, SWARM software is providing you with any data needed for traffic counting - The counts of vehicles including classification can be covered. The counts are split between two directions (IN/OUT). Furthermore, several counts can be made in one video camera, e.g.: count each lane separately. On top, you have the opportunity to add a second counting line, calibrate the distance in between and estimate the speed of the vehicles passing both lines.

What needs to be considered for a successful analysis?

Environment specification

Hardware Specifications

There are two major use cases for understanding the traffic situation across your city, urban streets as well as highways.

You want to know the flow of your intersections, SWARM Perception platform is providing the solution to get the number of vehicles starting in an origin zone and ending up in a destination zone per vehicle type (Classes).

What data can be generated?

For this use case, SWARM software is providing you with any data needed for Origin Destination Zones - The number of vehicles from one zone to another including classification according to BAST/TLS standards can be covered.

What needs to be considered for a successful analysis?

Environment specification

Hardware Specifications

Control Center

• Device Updates

  • Select individual devices for an update, e.g. this allows staged rollout site by site

  • Schedule the update, e.g. 5am to reduce the production impact to a minimum
• Device Camera View

  • Auto refresh the camera preview for a convenient calibration for up to 30seconds

  • The stream automatically expands when one stream is configured

  • Event triggers and focus zone is now visible in every camera preview

  • Show or hide the event triggers/focus area

  • The focus area is not hiding event triggers anymore
• The MQTT QoS level (0 - at most once, 1 - at least once, 2 -exactly once) for customer broker is configurable. Higher levels of QoS are more reliable, but involve higher latency and have higher bandwidth requirements.
• Side Menu
• API

  • Potentially breaking change: Authentication requests are throttled, limit: 100/hour. Note: The number of API calls for the Control Center is not throttled.

  • Potentially breaking change: Serial number format changed to string

Device

Models

• The detection accuracy for Traffic&Parking (Standard and Accuracy+) has been improved, in particular the classes: car with trailer, trucks (single unit, articulated, with trailer) and bicycle.

Tracker

We improve the data quality by enhancing the tracking system for:

• Non-linear movement (e.g. vehicles in roundabouts)

• Short occlusions (e.g. vehicles occluded by poles or signs)

• Track hijacking (one or more objects share the same track) when used in combination with the focus area

• Calibration visualisation

  • Tracks are coloured in blue when they are too small to classify.

  • Long tracks will not be cut-off

This is not a marketing video, it shows the engine overlay we ship with 2024.1. No hand tuning has been performed.

P101 diagram

You have a parking space where you simply want to know your utilization by making an Entry/Exit count, SWARM provides a perfect solution for doing that quite easily. See yourself:

What data can be generated?

For this use case, SWARM software is providing you with any relevant data for your Entry/Exit parking space. The solution is gathering the number of vehicles in your parking space as well as the number of vehicles entering and exiting your parking space for customizable time frames.

The vehicles are classified in any classes the SWARM software can detect. Nevertheless, consider that the following configuration set-up is optimized to detect vehicles and not people and bicycles.

What needs to be considered for a successful analysis?

Environment requirements

Hardware Specifications

The VPX agent needs the following hardware and software requirements to be met.

NVIDIA Jetson family

• Hardware

  • • Orin Nano 4GB/8GB, Orin NX 8GB/16GB, Orin AGX 32GB/64GB

  • Memory: at least 4 GB available

  • Storage: at least 6 GB free

• Software

NVIDIA GPUs

• Hardware

  • CPU: X86-64bit

  • Memory: At least 4 GB available

  • Storage: At least 15 GB free

  • NVIDIA Workstation GPUs

    • RTX series (e.g. RTX A2000)

    • Quadro RTX series (e.g. Quadro RTX 4000)

• Software

  • Ubuntu 20.04 LTS

  • NVIDIA Driver Version 470 (or newer)

  • Docker 19.0.1+

  • IotEdge 1.4

Install JetPack 5.1.2

Installation size

Install VPX Agent

With our installer script, installing the VPX agent is easy. Make sure to get the serial(s) from us in advance.

After the installation script is complete, the IoT Edge runtime will pull four docker containers as outlined below.

Make sure that the container curiosity-arm64-jetpack5 is used.

You will see in the SWARM Control Center an "Unnamed Device" with the corresponding registration ID:

Adaptive Traffic Control

Adaptive traffic control enables you to interface with hardware devices like traffic controllers using dry contacts. Use cases and benefits:

• 'Smart Prio' System: Prioritise certain traffic classes and ensure fluid traffic behavior in real-time (e.g. pedestrians, bicyclists, e-scooters, heavy traffic).

• Simplify infrastructure maintenance: Replace multiple induction loops with a single Swarm Perception Box. The installation does not require excavation work, and reduces the maintenance effort/costs.

Device Health - The healthiness of your device at a glance

The following metrics are supported:

• Device Uptime, Status, Restarts, Available Disk Space

• Device Temperature (support for P101/OP101/Jetson Nano)

• LTE Modem Traffic, Signal Strength and Reconnects (support for OP100/OP101)

• Camera status and Camera processing speed (FPS)

• Generated and Pending events

Model Improvements

Traffic & Parking (Standard and Accuracy+)

We improved the classification accuracy for both variants Standard and Accuracy+, especially for the classes: articulated truck, truck with trailer and car with trailer.

People Head

We fixed the class output, the event class output is now person (vs. previously head). Affected by this change are the devices P101/OP101/VPX. Not affected are the devices P100/OP100.

We improved the accuracy due to higher resolution processing.

People Full body

We improved the accuracy due to higher resolution processing.

Parking Fisheye

The model has been deprecated and will not be updated in the future. The model will continue to work as long as there are devices using it. Please consider switching to the Traffic & Parking model.

Device Metadata

Organize devices and generate events containing pre-defined device metadata. You can define up to five key and value pairs for a device. The keys and values can be freely defined by your choice, we support autocompletion for keys to avoid nasty typos.

Track Calibration History

Track calibration overlays the last 100 object tracks on a current camera frame. This enables you to position event triggers (e.g. counting lines) for optimal results. We extend the functionality with a history over the last 24 hours.

With track calibration history enabled you will be able to access the track calibration for every hour of the past 24 hours.

Device Update

You decide when your devices get an update. Please update soon in order to use the latest features (e.g. Adaptive Traffic Control, Track Calibration History) and to benefit from quality improvements (e.g. Model updates), bug fixes and security updates.

We will automatically update by 13.12.23.

Other improvements

• Control Center - User Management - Invite users and manage permission of existing users

• Control Center - MQTT client id can be defined for custom MQTT brokers

• Data Analytics - Speed-up of Origin-Destination widgets

• Data Analytics - Fix widget visualisation of the chord diagram (OD) and line charts

• Under some conditions an ROI in combination with a rule did not trigger events

Do you want to know the average speed of your traffic in given areas? SWARM software is providing the solution to get the number of vehicles passing at the street split in different speed segments (10km/h) or even see the average speed of the given count over an aggregated time period.

What data can be generated?

What needs to be considered for a successful analysis?

Environment specification

Hardware Specifications

How ANPR works

Automatic number plate recognition (ANPR) works in four steps. It needs to detect vehicles and license plates, read the plates, and trigger events.

1. Detect vehicle

Detect vehicles as cars, trucks, and buses and follow them in the video stream.

2. Detect license plate

For each detected vehicle, detect license plates and map them to the vehicles.

3. Read license plate

For each detected license plate, apply an optical character recognition (OCR) to read the plate.

4. Send event

If the vehicle crosses a counting line, send an event with the text from the detected license plate.

The main challenge for ANPR setups consists of clearly readable license plates. This means a sharp and well-illuminated image without occlusions or blurry objects is required to obtain correct results. The following guide shows how to set up our ANPR system and helps to avoid the most common issues.

Typical Setup

The system is designed for two typical setups which are described here.

Single lane from the side

For this setup, the camera is mounted at around 2m height, as closely as possible to the side of the lane to avoid a high horizontal angle. If possible, enforce vehicles to stay in lane for the ANPR section, as switching lanes can lead to inaccurate results.

Two lanes from above

When positioning the camera above the cars (e.g. entry/exit of a garage) a maximum of two lanes can be covered.

To work properly, vehicles should drive straight through the scene to have the license plate visible in the entire scene. The camera should be at a height of 3m and facing vehicles directly from the front or back to avoid a high horizontal angle to the license plate.

Camera Setup

Camera setup can sometimes be tricky and often requires some experimentation with the camera position and parameters to get optimal results.

The following sections describes common camera issues and how to avoid them.

Resolution

License plates need to be visible with 250 pixel-per-meter (PPM). For a standard European plate, this gives us a minimum height of 30px and a minimum width of 100px to get good recognition results.

For camera setups with object distances within the specification, a FullHD (1080p) resolution is sufficient. In some cases, it might help to choose a higher resolution (4MP or 2K) for a sharper image.

It is recommended to check the size of license plate crops manually during the setup phase.

Viewing Angle

License plates need to be visible from a direct viewing angle. While small angles (<20° horizontal, <30° vertical) and tilting <5° can be handled, larger angles cannot work at all. If view angles get bigger, the system is more likely to mix up characters or is not able to recognize characters close to the edges.

For camera positions from the side only a single lane is recommended, while with camera views from above, a maximum of two lanes works.

Lighting

Scene illumination has two major effects.

1. With good illumination, a lower shutter speed can be chosen and images get less blurred, especially for fast-moving vehicles.

2. Good lighting reduces the ISO value of the camera and images appear less grainy and sharper.

Some cameras offer additional illumination which can be useful. If the camera light is not sufficient, an external illumination of the scene is required.

Digital noise reduction (DNR) should be kept in a low range to further reduce graininess.

Shutter speed

A low shutter speed is important for moving objects to get a sharp image and avoid blurriness caused by motion.

While in general, faster is better, the selected shutter speed depends on the available light in the scene.

Depending on vehicle speed a shutter speed of 1/250 is a bare minimum for moving objects below 15 km/h. For faster vehicles, up to 40 km/h, a shutter speed of 1/500 is a good choice. For faster objects, an even lower shutter speed is required which only works with good illumination.

Encoding Quality

To stream the camera image, data is encoded. Different encodings can save data and reduce image quality. For the ANPR use case, high image quality is required. Select H.264 codec and a high bitrate of >6000kbps for FullHD (1080p) content and >8000 kbps for 4MP video material with 25 FPS.

Additional features such as BLC and WDR are not recommended, as postprocessing can reduce details. If they are necessary, the impact on the video quality should be checked.

A constant bitrate (CBR) usually leads to a better quality than variable bitrate (VBR).

Test setup in various conditions

When setting up the camera, it is recommended to take a few short test videos in different lighting conditions (morning, midday, evening, night) to check if license plates are clearly visible in all conditions.

Event Triggers

A suitable position of the event trigger (counting line) is essential for good ANPR results. If the line is positioned far in the back, the ANPR system has no time to detect and recognize the plate before an event is sent. If the line is in a position, where the license plate is only visible at a suboptimal angle, results will not be accurate.

For an optimal counting line position, a short debug video of the scene with 3-5 vehicles is required. In the analysis of the video, one should follow the vehicle through the optimal section with the best view on the plate (see Example 6). Just as the view on the plate gets worse (see Example 7), position the line right behind the center of the vehicle.

By this method, it’s guaranteed that the system can utilize the best video parts to detect and recognize the license plate and send the event just before suboptimal views worsen the result.

Accuracy

Our ANPR system is tested properly under various conditions. In our test setup, we have around five different scenes, and accuracies are calculated on the basis of > 800 European vehicles. Overall accuracy means the percentage of correctly identified vehicles plus license plates compared to all passing vehicles with readable license plates.

Under the specified conditions, the system reaches >95% overall accuracy in slow parking environments and >90% in environments with fast vehicles.

For a detailed analysis of potential errors, see limitations described below.

Limitations

A base limitation that cannot be solved is the general readability of license plates. Plates with occlusions, covered with dust or snow or incorrectly mounted plates cannot be read. Environmental limitations such as strong rain or snow which blocks the clear view on plates can also lead to inaccurate results.

General

There are a few hard limitations where the system cannot provide good results.

1. Illumination (day-only)

Currently, the system supports good illumination only. This limitation is usually for day-only, however it will also work for well-lighted night scenes if the license plates are clearly recognizable.

2. Single-line plates only

License plates with two lines (such as motorcycle plates) are not supported and recognition will not work.

3. EU license plates only

The recognition system is limited to standard EU license plates. It can work with license plates from other countries (and some older non-standard EU plates), but there are no accuracy guarantees.

Error Types

There are four potential errors that can occur within the ANPR system.

• No vehicle is detected (< 1% error rate)

• No plate is detected (< 0.1% error rate)

• Event is sent without a vehicle passing (< 0.1% error rate)

• Wrong plate text is recognized (< 5-10% error rate, depending on the scenario)

Typical Errors

The OCR system identifies character by character. In most error cases it’s a single character that is misclassified or a character that was missed. As in some countries, license plate characters look very similar (sometimes even exactly the same), most errors are caused by mixing up characters with lookalikes.

• B and 8 can be mixed-up

• D and 0 can be mixed-up

• 0 and O can be mixed-up

• I and 1 can be mixed-up

• 5 and S can be mixed-up

The best option to avoid these mixups is to get a clear front view on the plate. However, for systems that need to match in- and outgoing license plates, it might make sense to match them with a fuzzy search that takes mixups and duplicated characters into account. For example, the system could still match plate texts like S123A0 and SI23AO, when the second event exists on the same or following day.

Further improvements

If all setup recommendations are implemented and the camera configuration cannot be improved any further, there are some external improvements that can be made. Best results are achieved when combined.

1. Slow down vehicles in the ANPR section to have more time to detect and recognize the license plate.

2. Reduce distance from vehicle to camera, for example by limiting the entry to a single or narrow lane, reducing variation and angle to the camera.

3. If possible, use camera zoom to focus on the section with the best view on license plates. This can also help with a low resolution of plate crops.

4. If the Swarm system performance is an issue (low FPS), it can help to blackout any unnecessary image parts with vehicles (e.g. with a privacy zone). By this, the focus of the system is set on the ANPR section only.

You have a parking space where you want to know your utilization and parking times of your customers, then you can use the SWARM solution as following.

What data can be generated?

For this use case, SWARM software is providing you with any relevant data for your Entry/Exit parking space. The solution is gathering the number of vehicles in your parking space as well as the number of vehicles entering and exiting your parking space for customizable time frames.

The vehicles are classified in any classes the SWARM software can detect. Nevertheless, consider that the following configuration set-up is optimized to detect vehicles and not people and bicycles.

What needs to be considered for a successful analysis?

Find below some general settings for the installation of this use case. As the automatic number plate reading needs some more detailed information you will find additional and more detailed information on how to set it up in the following page:

Environment requirements

Hardware specification

Next to the traffic frequency at given locations, you are wondering about the length of a queue when traffic congestion is given. In combination with the speed of the detected vehicle, you can get proper insights into the length and speed of the current queue.

What data can be generated?

For this use case, SWARM software is providing you with the most relevant traffic insights - The counts of vehicles including the classification of the SWARM main classes can be covered. On top, you have the opportunity to add a second counting line, calibrate the distance in between and estimate the speed of the vehicles passing both lines. By combining this with different Regions of Interest (RoI) you can retrieve the needed insights into traffic congestion.

For traffic frequency, all SWARM main classes can be generated. Depending on the camera settings, we can detect present vehicles up to 70 m.

What needs to be considered for a successful analysis?

Possible cameras for this use case

Environment specification

Hardware Specifications

Perception Box Management

To open the configuration page of a Perception Box, click on the row of the Box. There you can manage all cameras running on one device.

Depending on your subscription, you will have a pre-defined number of cameras you may use with your Perception Box. If you need to process more cameras, contact our Sales team.

Camera settings

Clicking on a camera will collapse the corresponding settings. You can name the camera. On top you have the option to deactivate the camera stream. If a stream is deactivated it will not be taken into consideration by the SWARM Software and the performance will not be impacted but the configuration will be kept.

A GPS coordinate needs to be set for each camera. The GPS coordinate is mandatory and can by entering the coordinates or with the location picker directly on the map.

We are currently able to process camera streams over RTSP, as well as streams coming over USB. You can select the different options as Connection Type.

• USB cameras must be available as V4L device at /dev/video0. The following specifications are supported:

  • RAW color format: UYVY, YUY2, YVYU

  • Resolution: 1080p

  • Other camera settings:

    • Shutter speed, brightness, FPS are camera/sensor dependent and have to be individually calibrated for optimal results

  • Make sure to use USB 3.0 camera in order to benefit from the full frame rate.

The other fields for the Camera Connection can be found in the manual of the camera and/or can be configured on the camera itself.

Camera monitoring status

In the device configuration, you have seen the overall status of the cameras included in one Perception Box. On the camera level, you have the option to see the individual status to better identify the root cause of the issue (see mark 4 in the overview above).

Adaptive Traffic Control

Adaptive traffic control enables you to interface with hardware devices like traffic controllers using dry contacts. Use cases and benefits:

• 'Smart Prio' System: Prioritise certain traffic classes and ensure fluid traffic behavior in real-time (e.g. pedestrians, bicyclists, e-scooters, heavy traffic).

• Simplify infrastructure maintenance: Replace multiple induction loops with a single Swarm Perception Box. The installation does not require excavation work, and reduces the maintenance effort/costs.

What needs to be considered for a successful analysis?

Environment specification

Hardware Specifications

You have a parking space where you simply want to know if your specific parking spaces are occupied or free, SWARM provides a perfect solution for doing that quite easily. See yourself:

What data can be generated?

For this use case, SWARM software is providing you with any relevant data for Single Space detection within your parking space. The solution is to provide you with the occupancy state of each of your configured parking lots.

The single space detection will give you information about the occupancy state of your parking lot (free or occupied) as well as the information about the object in your parking space, including the classification. Nevertheless, consider that the following configuration set-up is optimized to detect vehicles and not people and bicycles. On top the classification is depending on the camera installation, for a more top-down view the classification will be less accurate.

Camera placement

• put a car on one of the parking spaces

• put a large vehicle (high van, small truck - the largest vehicle that you expect in your parking) on all parking spaces next to your car

• if you still can see >70 % of the car, then this parking spot is valid.

General & easy recommendations for deciding where to place the camera:

• Parking spots have to be fully visible (inside the field of view of the camera). We do not guarantee full accuracy for cropped single parking spaces.

• Avoid objects (trees, poles, flags, walls, other vehicles) that occlude the parking spaces. Avoid camera positions, where cars (especially high cars like vans) occlude other cars.

• Occlusions by other parking cars, mainly happen if parking spaces are aligned in direction of camera-alignment lines.

Get a better overview for installations with more details on camera distance to objects and mounting height:

What needs to be considered for a successful analysis?

Environment requirements

Hardware Specifications

Now, as you see your camera, you have the option to configure it. This is where the magic happens!

Event Triggers

Each event trigger will generate a unique ID in the background. In order for you to keep track of all your configured types, you are able to give it a custom name on the left side panel of the configuration screen. --> This name is then used for choosing the right data in Data Analytics.

Please find the abbreviation and explanation of each event type below.

Event Types

We provide templates for the three different areas in order to have everything set for your use case.

• Parking events --> Templates for any use case for Parking monitoring

• Traffic events --> Templates for use cases around Traffic Monitoring and Traffic Safety.

• People events --> Templates for using the People Full Body or People Head model.

This will support you to easier configure your scene with the corresponding available settings. You can find the description of the available Event Triggers and the individual available Trigger Settings below.

Event Triggers Details

Global Settings

Configuration of the Event Triggers

To create your own solution, select a model for your solution and then place your type (or select raw tracks mode).

When a type is active, left-click and hold the white circles to move the single corner points. You can create any tetragon (four-sided polygon). To move the entire type, left-click and hold anywhere on the type.

The essence of our computer vision engine's ability to detect and classify lies in its models.

Which model should I use?

Traffic & Parking (Standard and Accuracy+)

Class Definition

Event will contain class and subclass according to this definition.

The following definitions will be used:

• Camera-angle: measured from a horizontal line.

• Camera-height: mounting height of the camera

• Distance camera to point in the image-center. This distance is already fixed with camera angle and camera mounting height. But for better estimation of your camera setup, we add it as an additional parameter.

In the following, we give standard examples, with the following assumptions:

• Car-width = 2 m

• Parking length/car-length = 5 m

• Height of the occluding vehicle = 2.5 m

Single parallel parking line

Two parallel parking lines

Device Schedule (Running/Standby)

The device schedule allows you to define time slots when the device should be in operational mode (device state: running) or in power saving mode (device state: standby).

The power saving mode is mostly relevant for the battery powered system BMA Mobil since it extends the usage time for a single charge.

In our measurements, the BMA Mobil reduces the power consumption during standby by about 55%.

How to enable the schedule for a device?

1. Open a device in the control center and select the tab schedule

2. Switch the toggle to Enabled and select desired time slots when the device should be operational

3. Save Schedule

What will happen during standby?

The device reduces the power consumption by pausing the video signal processing.

Functions that are not available during standby:

• No events will be generated

• No camera image is visible

• No event triggers can be configured

Functions that are available during standby:

• The device will be online in the Control Center, including available device health, logs, and reboot functionality.

• Wake-up the device from standby (→Set the toggle to Disabled)

What is the expected battery lifetime?

In our measurements the BMA Mobil consumes 1,07A when active and 0,48A when in standby.

Examples

With an 100Ah battery degraded to 90Ah we get the following battery lifetimes:

• Always active: 90Ah / 1,07A ~3,5 days

• Always standby: 90Ah / 0,48A ~7,8 days

• 13h active, 59h standby (Example from screenshot, total 3days): 13h * 1,07A + 59h *0,48A = 42Ah out of 90Ah (~50%)

Improved latency for BMA/BMC

We have been able to improve latency for the BMA working together with the BMC to < 1s. Measured by vehicle is physically present in a zone to the point when the BMC switches a dry contact.

Other Improvements

• Fixed: On Curiosity startup the first Region of Interest event was always vacant (even if there were objects).

• Improved: Curiosity performance improvements by avoiding to re-classify already classified objects

• Improved: The Number Plate Column for DataAnalytics Parking widgets is optional

Device Update Size

2023.3 U1 → 2024.2: 155MB (BMA / P101 / Jetson Nano)

API

• There are no API breaking changes

Next to the traffic frequency at given locations, you are wondering about statistics on how long the vehicles take from one to another location and how the traffic flows across your city and municipality. With this solution, you can generate that data with a single sensor solution from SWARM.

What data can be generated?

For this use case, SWARM software is providing you with the most relevant traffic insights - The counts of vehicles including the classification of the SWARM main classes can be covered. On top, you have the opportunity to add a second counting line, calibrate the distance in between and estimate the speed of the vehicles passing both lines. By combining more sensors in different locations the journey time as well as the statistical traffic flow distribution will be generated.

The journey time and traffic flow distribution can be generated for vehicles only (car, bus and truck).

What needs to be considered for a successful analysis?

Environment specification

Accuracy

In this technical documentation, accuracy refers to the penetration rate of a single sensor, which is the percentage of correctly identified license plates divided by the total number of vehicles counted during a ground truth count.

The current penetration rate for this use case is 60%, taking into account different day/nighttimes, weather conditions, and traffic situations. When calculating journey time between two sensors, approximately 36% of journeys are used as the baseline, which is calculated by multiplying the penetration rate of both sensors.

The accuracy is sufficient to generate data that can be used to make valid conclusions about vehicle traffic patterns and journey times.

Hardware Specifications