video-object-tracking

Efficiently Count Escalator Passengers with AI-Driven Actions

25 Apr 2025
Efficiently Count Escalator Passengers with AI-Driven Actions

In today's fast-paced urban environments, understanding foot traffic is crucial for optimizing infrastructure and improving user experience. The Escalator Passenger Counting service leverages advanced Cognitive Actions to provide developers with the tools to analyze video footage and accurately count the number of passengers using escalators. By automating this process, businesses can gain valuable insights into user patterns, enhance safety measures, and improve operational efficiency.

This service is particularly beneficial for scenarios such as monitoring crowd density in shopping malls, train stations, or airports, where escalators are commonly used. With the ability to analyze video data in real-time, developers can implement this technology into their applications, leading to smarter decision-making and resource allocation.

Prerequisites

To get started with the Escalator Passenger Counting service, you'll need a Cognitive Actions API key and a basic understanding of API calls to integrate the functionality into your applications.

Count Escalator Passengers

This action is designed to analyze video footage to count the number of passengers on an escalator. By utilizing provided Region of Interest (ROI) masks and coordinate inputs, it ensures precise tracking and counting within defined boundaries.

Input Requirements

To use this action, you'll need to provide:

  • File: A URI of the input mask image, which defines the areas of interest.
  • Video: A URI of the input video file that captures the escalator activity.
  • Lower Zone Coordinates: Coordinates for the lower boundary of the escalator in the format "x3 y3 x4 y4". The default is "527 489 735 489".
  • Upper Zone Coordinates: Coordinates for the upper boundary in the format "x1 y1 x2 y2". The default is "103 261 396 261".

Example Input:

{
  "file": "https://replicate.delivery/pbxt/JTpu1mMG1v7QbMMwdnainKWkyeYqbJAGPnprrFSP6ZBTX2dh/mask.png",
  "video": "https://replicate.delivery/pbxt/JTpu269yl19TRGpfVaIWtZnYNOIz2nDZ8U0lib5TEvOnNfcf/people.mp4",
  "lowerCoordinates": "527 489 735 489",
  "upperCoordinates": "103 261 396 261"
}

Expected Output

The output will be a processed video that highlights the counted passengers, allowing you to visualize the foot traffic on the escalator. You will receive a URI link to the output video, which showcases the counting process.

Example Output:

https://assets.cognitiveactions.com/invocations/a8116150-0ff6-4006-9c10-cb4c0d487176/e2c5736b-6cba-49cb-a236-de3ad1bbea65.mp4

Use Cases for this Specific Action

  • Retail Analytics: Retailers can analyze traffic patterns to optimize store layouts and staffing.
  • Safety Monitoring: Transportation hubs can monitor escalator usage to ensure safety and compliance with capacity regulations.
  • Urban Planning: City planners can use the data to make informed decisions about infrastructure improvements in high-traffic areas.

```python
import requests
import json

# Replace with your actual Cognitive Actions API key and endpoint
# Ensure your environment securely handles the API key
COGNITIVE_ACTIONS_API_KEY = "YOUR_COGNITIVE_ACTIONS_API_KEY"
# This endpoint URL is hypothetical and should be documented for users
COGNITIVE_ACTIONS_EXECUTE_URL = "https://api.cognitiveactions.com/actions/execute"

action_id = "1802f26a-9b66-4f82-9659-4230c4260a0a" # Action ID for: Count Escalator Passengers

# Construct the exact input payload based on the action's requirements
# This example uses the predefined example_input for this action:
payload = {
  "file": "https://replicate.delivery/pbxt/JTpu1mMG1v7QbMMwdnainKWkyeYqbJAGPnprrFSP6ZBTX2dh/mask.png",
  "video": "https://replicate.delivery/pbxt/JTpu269yl19TRGpfVaIWtZnYNOIz2nDZ8U0lib5TEvOnNfcf/people.mp4",
  "lowerCoordinates": "527 489 735 489",
  "upperCoordinates": "103 261 396 261"
}

headers = {
    "Authorization": f"Bearer {COGNITIVE_ACTIONS_API_KEY}",
    "Content-Type": "application/json",
    # Add any other required headers for the Cognitive Actions API
}

# Prepare the request body for the hypothetical execution endpoint
request_body = {
    "action_id": action_id,
    "inputs": payload
}

print(f"--- Calling Cognitive Action: {action.name or action_id} ---")
print(f"Endpoint: {COGNITIVE_ACTIONS_EXECUTE_URL}")
print(f"Action ID: {action_id}")
print("Payload being sent:")
print(json.dumps(request_body, indent=2))
print("------------------------------------------------")

try:
    response = requests.post(
        COGNITIVE_ACTIONS_EXECUTE_URL,
        headers=headers,
        json=request_body
    )
    response.raise_for_status() # Raise an exception for bad status codes (4xx or 5xx)

    result = response.json()
    print("Action executed successfully. Result:")
    print(json.dumps(result, indent=2))

except requests.exceptions.RequestException as e:
    print(f"Error executing action {action_id}: {e}")
    if e.response is not None:
        print(f"Response status: {e.response.status_code}")
        try:
            print(f"Response body: {e.response.json()}")
        except json.JSONDecodeError:
            print(f"Response body (non-JSON): {e.response.text}")
    print("------------------------------------------------")



## Conclusion
The Escalator Passenger Counting service offers developers a powerful tool for understanding foot traffic dynamics in various environments. By automating the counting process, businesses can make data-driven decisions that enhance user experience and operational efficiency. With a straightforward API integration, you can start leveraging this technology immediately, paving the way for smarter infrastructure management and planning. Consider implementing this service to gain insights that can transform how you understand and respond to user behavior.
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