Occupancy monitoring plays a key role in building automation, health, safety, and security. Although developers can piece together suitable people-counting solutions from available components and develop the appropriate algorithms, this can be time consuming and costly. Amid heightened expectations for faster delivery of solutions with more sophisticated and up-to-date capabilities and features, including support for social distancing requirements, a simpler, faster approach is required.

Figure 1. In Analog Devices’ EagleEye trial kit, a DSP subsystem acquires and processes images using the ADSW4000 EagleEye PeopleCount algorithm running on a member of Analog Devices’ ADSP-BF707 Blackfin DSP series. (Image: Analog Devices)

Why Occupancy Monitoring Matters

The ability to monitor the number of individuals, their location, and their movement within a building is finding an expanding role in multiple applications. During pandemic surges, this capability helps ensure that occupants can maintain safe separation in indoor spaces.

Beyond optimizing use of building spaces and aiding social distancing, however, an active measure of occupancy has become essential to throttle escalating energy consumption. According to the World Green Building Council, energy used to light, heat, and cool buildings accounts for 28% of worldwide carbon emissions.

Globally, national strategies to reduce climate-affecting carbon emissions target more efficient building energy utilization as central to the planning. For individual companies, reduced energy consumption offers direct benefits to their bottom line, as well as their employees’ well-being.

Implementing an Occupancy Sensing Solution

The design and implementation of an automated occupancy sensing solution requires expertise in multiple areas in order to combine sensors, low-power processors, and connectivity with accurate people-counting algorithms into full applications, able to respond instantly as people enter and leave indoor spaces.

This takes time and resources to develop and support. Analog Devices offers a pre-packaged solution: the ADSW4000 EagleEye, a complete 2D vision sensor-based, low-power, low-bandwidth drop-in platform designed specifically to provide updated data to optimize space utilization and minimize energy consumption. The kit comprises the Analog Devices’ proprietary People-Count algorithm running on a member of Analog Device’s ADSP-BF707 series of Blackfin digital signal processors (DSPs). The EagleEye delivers utilization data for separate indoor spaces, allowing companies to balance office space utilization and energy consumption for maximum utility.

Because it performs its image analysis and people-counting tasks solely on the Blackfin processor, the EagleEye algorithm ensures that all images remain local, so no personally identifiable information ever leaves the platform, conforming with a growing body of worldwide privacy regulations. In fact, results generated by the Blackfin processor are limited to a data package containing the number of people in a monitored region of interest (ROI), their (x, y) location in that region, and whether or not they are on the move.

To help speed development of plications, Analog Devices integrates its ADSW4000 EagleEye People-Count platform in its EVAL-ADSW4000KTZ EagleEye trial kit. Serving as a complete sensor-to-cloud turnkey implementation of its EagleEye algorithm, the trial kit enables users to immediately deploy occupancy monitoring using the available app and cloud-based online dashboard. Alternatively, the kit can serve as the foundation of custom systems, allowing developers to focus on their higher-level applications rather than the details of implementing their own people-counting methods.

Individual Subsystems

The trial kit comprises a pair of subsystems, one based on the Blackfin DSP to generate people- counting data, and a separate subsystem based on Analog Devices’ ADuCM4050 microcontroller unit (MCU) to handle connectivity and higher-level application functionality (Figure 1).

For image acquisition from the region of interest, the subsystem uses a 2D vision sensing module based upon ON Semiconductor’s ASX340AT3C00X-PED0-DPBR VGA-format CMOS active pixel digital image system-on-chip (SoC) sensor combined with an infrared filter.

Working with Analog Devices’ Eagle-Eye framework services, the EagleEye PeopleCount ADSW4000 algorithm runs on the ADSP-BF707 Blackfin DSP using ISSI’s IS25LP512M 512-megabit serial flash memory and Micron Technology’s MT46H64M16LF 1-gigabit low-power double data rate (DDR) synchronous dynamic random-access memory (SDRAM).

In this subsystem, the ADSP-BF707 Blackfin DSP is well suited for handling the complex image acquisition and processing tasks required for people-counting. Its signal processing pipeline includes multiple hardware multiply-accumulate (MAC) units along with single instruction, multiple-data (SIMD) capabilities.

The system achieves up to a 90% accurate count within the target area — and just as important, it returns the results quickly.

Latency is similarly low for generated people-count and location data. The algorithm provides updated people-count and location data within 1.5 seconds after an individual moves into a zone defined by the user during commissioning. After detecting an individual, the algorithm needs only 113 ms to provide updated count and location data.

Table 1: Analog Devices’ EagleEye algorithm maintains users’ privacy by not transmitting personally identifiable information, but instead generating a package that includes the metadata listed here. (Image: Analog Devices)

The DSP uses its universal asynchronous receiver-transmitter (UART) port in push mode to transmit occupancy metadata. Transmitted in JSON format, this metadata packet includes occupancy state (occupied or vacant), people-count, people-location as (x, y) coordinates, along with other data (Table 1).

Downstream of the DSP subsystem, the ADuCM4050 MCU subsystem runs in the AWS FreeRTOS environment, supporting the high-level EagleEye application and connectivity services required for sensor commissioning and communication with Analog Devices’ associated cloud-based service.

A set of integrated power management features including multiple power modes and clock gating capabilities enable the device to achieve low power execution. As a result, the MCU requires only 41 microamps per megahertz (μA/ MHz) (typical) in active mode and 0.65 μA (typical) in hibernation mode. During inactive periods, the processor consumes only 0.20 μA (typical) in its fast-wake-up shutdown mode, or only 50 nA in full shutdown mode.

How to Quickly Begin People-Counting

Figure 2. Analog Devices’ EagleEye PeopleCount app allows easy confirmation of the sensor unit placement prior to commissioning. (Image: Analog Devices)

In the trial kit, Analog Devices combines the DSP and MCU subsystems with a camera sensor, a lens, LEDs, and buttons in a compact package.

Developers can quickly deploy people-counting by simply mounting the sensor unit in a room or indoor space, directly above a region of interest. The sensor can use power from a variety of sources. Users can run a wire to the unit’s DC connector to supply a 5.5 to 36 volt DC source, or power it by supplying a USB power source using a micro USB cable, or active USB extension for distances beyond one meter.

After mounting the sensor unit, users can visually confirm the sensor positioning and desired field of view (FOV) using the companion EagleEye People-Count app (Figure 2).

After users verify the sensor FOV, they proceed with the brief device commissioning process. During commissioning and later during operation, users can observe the DSP and MCU LEDs built into the sensor unit to monitor the current status of the respective subsystems.

Figure 3. During commissioning, users employ the companion app to identify areas that the EagleEye PeopleCount algorithm should examine (left) or ignore (right). (Image: Analog Devices)

The app walks users through the few steps required for sensor commissioning. In this process, users indicate which areas the algorithm should monitor within the FOV by marking a series of inclusive masks, such as the floor mask (Figure 3, left). Areas to exclude are just as important for accurate counts. During the commissioning process, the companion app allows users to specify different exclusion masks, for example, windows and display screens (Figure 3, right).

Once mounted and commissioned, the sensor unit begins transmitting its metadata to Analog Devices’ cloud. By logging into the cloud using credentials provided during registration, users can examine a series of graphical representations of occupancy.

The platform can be embedded in custom designs built with the appropriate Blackfin processor and suitable external flash memory. Analog Devices also makes the EagleEye software package available for registered trial kit customers.

What Can Be Done with Occupancy Data?

Once building occupancy sensors are set up, the data can be used in many ways:

  • During this pandemic age, the data can be used to see if proper social distancing is being observed with the “People ID with Properties” metadata output listed in Table 1.

  • If the EagleEye detects that a room is occupied, the data can be used by a smart building system to ensure that the HVAC environmental settings are appropriate for the number of people in the room and that there is proper lighting.

  • If the EagleEye detects that a room is unoccupied, the data can be used by a smart building system to set the HVAC environmental settings accordingly and turn off the lights.

  • If a building evacuation is necessary due to a fire or some other emergency, the occupancy sensor could alert the proper personnel through a smart building system that the building is still occupied and the location of the occupants.


As companies pay the price of significant building energy consumption from office lighting, heating, and cooling, effective resource management of often vacant office spaces is driving a need for more accurate occupancy data. Based on a proprietary algorithm running on a low-power digital signal processor, the Analog Devices ADSW4000KTZ Trial Kit provides a comprehensive sensor-to-cloud platform for evaluating and deploying the real-time occupancy monitoring with room-level data needed for more effective building energy management.

This article was written by Rich Miron, Sr. Technical Content Developer, Digi-Key Electronics. For more information, contact Mr. Miron at This email address is being protected from spambots. You need JavaScript enabled to view it. or visit here .