RooDe

People counter working with any smart home system which supports ESPHome and therefore Home Assistant. All necessary entities are created automatically.

• Hardware Recommendation
• Wiring
  • ESP32
  • ESP8266
• Configuration
  • Configuration variables
  • Sensor
  • Threshold distance
• Algorithm
• FAQ/Troubleshoot

Hardware Recommendation

• ESP8266 or ESP32
  • Wemos D1 Mini ESP32 <-- Recommended
  • Wemos D1 mini (ESP8266)
  • NodeMCU V2
• 1x VL53L1X
  • Pololu <-- Recommended
  • GY-53
  • Black PCB chinese sensor
• 1A Power Supply Do not use an USB port of your computer!
• Encolsure (see .stl files) - will be updated soon! Pins: SDA_PIN 4 (ESP8266) or 21 (ESP32) SCL_PIN 5 (ESP8266) or 22 (ESP32)

Wiring

The sensors from Pololu, Adafruit and the GY-53 can also be connected to the 5v pin (VIN) as they have an voltage regulator.

If you use a GY-53 you need to connect GND the PS (Ps=0) pin.

Ps=1 (default): Serial port UART mode, Pin3 is TX, Pin4 is RX, TTL level, PWM output works. Ps=0 (when connected to GND): In the IIC mode, the user can operate the chip by himself. The module owns the MCU and does not operate the chip. The PWM output does not work.

ESP32

                    ESP32   VL53L1X board
-------------------------   -------------
                      3V3 - VIN
                      GND - GND
     SDA (pin 42, GPIO21) - SDA
     SCL (pin 39, GPIO22) - SCL

ESP8266

                  ESP8266   VL53L1X board
-------------------------   -------------
                      3V3 - VIN
                      GND - GND
              D2 (GPIO 4) - SDA
              D1 (GPIO 5) - SCL

Configuration

Roode is provided as an external_component which means it is easy to setup in any ESPHome sensor configuration file.

Example configuration

roode:
  id: roode_platform
  i2c_address: 0x29
  update_interval: 200ms
  calibration:
    max_threshold_percentage: 85
    min_threshold_percentage: 5
    roi_calibration: true
  # manual:
  #   sensor_mode: 2
  #   roi_height: 16
  #   roi_width: 6
  #   manual_threshold: 1300
  #   timing_budget: 100
  invert_direction: true

number:
  - platform: roode
    people_counter:
      name: People Count

Configuration variables

• i2c_address (Optional, integer): The I²C address of the sensor. Defaults to 0x29.
• update_interval (Optional, Time): The interval to check the sensor. Defaults to 100ms.
• calibration (Optional, exclusive-mode): Enables automatic zone calibration:
  • max_threshold_percentage (Optional, int): The maxium threshold in % which needs to be reached to detect a person. Min: 50 Max: 100. Defaults to 85.
  • min_threshold_percentage (Optional, int): The minimum threshold in % which needs to be reached to detect a person. Min: 0 Max: 100. Defaults to 0.
  • roi_calibration (Optional, bool): Enables automatic ROI calibration (experimental). Defaults to false.
• manual (Optional, exclusiv-modee): Enables manual sensor setup:
  • manual_threshold (required, int): The threshold for both zones. Min: 40 Max: 4000. Defaults to 2000.
  • roi_height (required, int): The height of the ROI zones. Min: 4 Max: 16. Defaults to 16.
  • roi_width (required, int): The height of the ROI zones. Min: 4 Max: 16. Defaults to 6.
  • sensor_mode(required, int): Sets the distance mode of the sensor if calibration=false.
    • Options: 0=short, 1=long, 2=max. Defaults to true.
  • timing_budget (optional, int): The timing budget for the sensor. Increasing this slows down detection but increases accuracy. Min: 10ms Max: 1000s. Defaults to 10ms.
• invert_direction (Optional, bool): Inverts the counting direction. Switch to true if the movement count appears backwards. Defaults to false.
• advised_sensor_orientation(Optional, bool): Advised orientation has the two sensor pads parallel to the entryway. So false means the pads are perpendicular to the entryway. Defaults to true.

Sensor

Example Sensor setup to use all available features:

binary_sensor:
  - platform: status
    name: $friendly_name Status
  - platform: roode
    presence_sensor:
      name: $friendly_name presence

sensor:
  - platform: roode
    id: hallway
    distance_sensor:
      name: $friendly_name distance
      filters:
        - delta: 100.0
    threshold_zone0:
      name: $friendly_name Zone 0
    threshold_zone1:
      name: $friendly_name Zone 1
    roi_height:
      name: $friendly_name ROI height
    roi_width:
      name: $friendly_name ROI width

  - platform: wifi_signal
    name: $friendly_name RSSI
    update_interval: 60s

  - platform: uptime
    name: Uptime Sensor
    id: uptime_sensor
    update_interval: 120s
    internal: true
    on_raw_value:
      then:
        - text_sensor.template.publish:
            id: uptime_human
            state: !lambda |-
              int seconds = round(id(uptime_sensor).raw_state);
              int days = seconds / (24 * 3600);
              seconds = seconds % (24 * 3600);
              int hours = seconds / 3600;
              seconds = seconds % 3600;
              int minutes = seconds /  60;
              seconds = seconds % 60;
              return (
                (days ? String(days) + "d " : "") +
                (hours ? String(hours) + "h " : "") +
                (minutes ? String(minutes) + "m " : "") +
                (String(seconds) + "s")
              ).c_str();

text_sensor:
  - platform: roode
    version:
      name: $friendly_name version
  - platform: roode
    entry_exit_event:
      name: $friendly_name last direction

  - platform: template
    name: $friendly_name Uptime Human Readable
    id: uptime_human
    icon: mdi:clock-start

Threshold distance

Another crucial choice is the one corresponding to the threshold. Indeed a movement is detected whenever the distance read by the sensor is below this value. The code contains a vector as threshold, as one (as myself) might need a different threshold for each zone.

The threshold is automatically calculated by the sensor. To do so it is necessary to position the sensor and, after turning it on, wait for 10 seconds without passing under it. After this time, the average of the measures for each zone will be computed and the thereshold for each ROI will correspond to 80% of the average value. Also the value of 80% can be modified in the code, by editing the variable max_threshold_percentage and min_threshold_percentage.

If you install the sensor e.g 20cm over a door you dont want to count the door open and closing. In this case you should set the min_threshold_percentage to about 10.

Example:

Mounting height:    2200mm
Door height:        2000mm
Person height:      1800mm
max_threshold_percentage: 80% = 1760
min_threshold_percentage: 10% = 200

All distances smaller then 200mm and greater then 1760mm will be ignored.

Algorithm

The implemented Algorithm is an improved version of my own implementation which checks the direction of a movement through two defined zones. ST implemented a nice and efficient way to track the path from one to the other direction. I migrated the algorigthm with some changes into the Roode project. The concept of path tracking is the detecion of a human:

• In the first zone only
• In both zones
• In the second zone only
• In no zone

That way we can ensure the direction of movement.

The sensor creates a 16x16 grid and the final distance is computed by taking the average of the distance of the values of the grid. We are defining two different Region of Interest (ROI) inside this grid. Then the sensor will measure the two distances in the two zones and will detect any presence and tracks the path to receive the direction.

However, the algorithm is very sensitive to the slightest modification of the ROI, regarding both its size and its positioning inside the grid.

ST Microelectronics define the values for the parameters as default like this:

The center of the ROI you set is based on the table below and the optical center has to be set as the pad above and to the right of your exact center:

Set the center SPAD of the region of interest (ROI) based on VL53L1X_SetROICenter() from STSW-IMG009 Ultra Lite Driver

ST user manual UM2555 explains ROI selection in detail, so we recommend reading that document carefully. Here is a table of SPAD locations from UM2555 (199 is the default/center):

128,136,144,152,160,168,176,184,  192,200,208,216,224,232,240,248
129,137,145,153,161,169,177,185,  193,201,209,217,225,233,241,249
130,138,146,154,162,170,178,186,  194,202,210,218,226,234,242,250
131,139,147,155,163,171,179,187,  195,203,211,219,227,235,243,251
132,140,148,156,164,172,180,188,  196,204,212,220,228,236,244,252
133,141,149,157,165,173,181,189,  197,205,213,221,229,237,245,253
134,142,150,158,166,174,182,190,  198,206,214,222,230,238,246,254
135,143,151,159,167,175,183,191,  199,207,215,223,231,239,247,255

127,119,111,103, 95, 87, 79, 71,   63, 55, 47, 39, 31, 23, 15,  7
126,118,110,102, 94, 86, 78, 70,   62, 54, 46, 38, 30, 22, 14,  6
125,117,109,101, 93, 85, 77, 69,   61, 53, 45, 37, 29, 21, 13,  5
124,116,108,100, 92, 84, 76, 68,   60, 52, 44, 36, 28, 20, 12,  4
123,115,107, 99, 91, 83, 75, 67,   59, 51, 43, 35, 27, 19, 11,  3
122,114,106, 98, 90, 82, 74, 66,   58, 50, 42, 34, 26, 18, 10,  2
121,113,105, 97, 89, 81, 73, 65,   57, 49, 41, 33, 25, 17,  9,  1
120,112,104, 96, 88, 80, 72, 64,   56, 48, 40, 32, 24, 16,  8,  0 <- Pin 1

This table is oriented as if looking into the front of the sensor (or top of the chip). SPAD 0 is closest to pin 1 of the VL53L1X, which is the corner closest to the VDD pin on the Pololu VL53L1X carrier board:

  +--------------+
  |             O| GPIO1
  |              |
  |             O|
  | 128    248   |
  |+----------+ O|
  ||+--+  +--+|  |
  |||  |  |  || O|
  ||+--+  +--+|  |
  |+----------+ O|
  | 120      0   |
  |             O|
  |              |
  |             O| VDD
  +--------------+

However, note that the lens inside the VL53L1X inverts the image it sees (like the way a camera works). So for example, to shift the sensor's FOV to sense objects toward the upper left, you should pick a center SPAD in the lower right.

FAQ/Troubleshoot

Question: Why is the Sensor not measuring the correct distances?

Answer: This can happen in various scenarios. I try to list causes sorted by likelyhood

1. You did not remove the protection film (most times its yellow)
2. You did not connect the Sensor properly
3. Light interference (You will see a lot of noise)
4. Bad connections

Sponsors

Thank you very much for you sponsorship!

• sunshine-hass