Design a system to detect people near a door and predict if they will enter.

System Design: Door Entry Prediction

This document outlines a system design for detecting people near a door and predicting whether they will enter. The system leverages computer vision, machine learning, and various sensors to achieve accurate and timely predictions.

1. Requirements

Use Cases

• Real-time Detection: The system must detect people approaching the door in real-time.
• Entry Prediction: The system must predict whether a detected person will enter the door with a high degree of accuracy.
• Data Logging: The system should log events, including detections, predictions, and actual entry events, for analysis and model improvement.
• Alerting: The system should provide alerts based on specific entry patterns or anomalies.

User Stories

• As a building manager, I want to track entry patterns to optimize staffing.
• As a security officer, I want to receive alerts for unusual entry attempts.
• As a researcher, I want to analyze entry data to understand pedestrian behavior.

2. High-Level Design

The system comprises the following components:

• Video Capture: Cameras placed strategically around the door capture video streams.
• Person Detection Module: A computer vision model detects people in the video frames.
• Tracking Module: The tracking module maintains the identity of detected people as they move within the camera's field of view.
• Feature Extraction Module: This module extracts relevant features from the video, such as speed, direction, proximity to the door, body language, and facial cues.
• Prediction Module: A machine learning model uses extracted features to predict whether a person will enter the door.
• Data Storage: A database stores detection events, predictions, and entry confirmations.
• Alerting System: This system triggers alerts based on predefined rules or anomalies detected in the data.

Component Interaction Diagram:

[Diagram: System components interacting with each other: Camera capturing video -> Person Detection -> Tracking -> Feature Extraction -> Prediction -> Data Storage and Alerting.]

3. Data Model

We will use a relational database (e.g., PostgreSQL) to store the data.

Tables

• Persons Table:

  Field	Type	Description
  person_id	UUID	Unique identifier for each person detected.
  first_seen	TIMESTAMP	Timestamp of when the person was first detected.
  last_seen	TIMESTAMP	Timestamp of when the person was last seen.

• Detections Table:

  Field	Type	Description
  detection_id	UUID	Unique identifier for each detection event.
  person_id	UUID	Foreign key referencing Persons table.
  timestamp	TIMESTAMP	Timestamp of the detection.
  x	FLOAT	X-coordinate of the person's bounding box.
  y	FLOAT	Y-coordinate of the person's bounding box.
  width	FLOAT	Width of the person's bounding box.
  height	FLOAT	Height of the person's bounding box.
  camera_id	VARCHAR	ID of the camera that made the detection.

• Features Table:

  Field	Type	Description
  feature_id	UUID	Unique identifier for each feature set.
  detection_id	UUID	Foreign key referencing Detections table.
  speed	FLOAT	Speed of the person in pixels/second.
  direction	FLOAT	Angle of the person's movement relative to the door.
  distance_to_door	FLOAT	Distance from the person to the door in pixels.
  body_language	TEXT	Description of body language (e.g., "looking at door").
  facial_cues	TEXT	Description of facial expression (if available).

• Predictions Table:

  Field	Type	Description
  prediction_id	UUID	Unique identifier for each prediction.
  detection_id	UUID	Foreign key referencing Detections table.
  timestamp	TIMESTAMP	Timestamp of the prediction.
  prediction	BOOLEAN	Predicted entry (TRUE for enter, FALSE for not).
  confidence	FLOAT	Confidence level of the prediction.
  model_version	VARCHAR	Version of the prediction model used.

• EntryEvents Table:

  Field	Type	Description
  event_id	UUID	Unique identifier for each entry event.
  person_id	UUID	Foreign key referencing Persons table.
  timestamp	TIMESTAMP	Timestamp of the entry event.
  entry	BOOLEAN	TRUE for entry, FALSE for exit.
  camera_id	VARCHAR	ID of the camera that detected the entry event.

4. Endpoints

Detection Endpoint

• Endpoint: /detect

• Method: POST

• Request:

  {
    "camera_id": "camera1",
    "image_data": "base64 encoded image",
    "timestamp": "2024-01-01T12:00:00Z"
  }
  

• Response:

  {
    "detections": [
      {
        "person_id": "uuid1",
        "x": 100,
        "y": 200,
        "width": 50,
        "height": 100
      }
    ]
  }
  

Prediction Endpoint

• Endpoint: /predict

• Method: POST

• Request:

  {
    "detection_id": "uuid1"
  }
  

• Response:

  {
    "prediction": true,
    "confidence": 0.85
  }
  

Alert Endpoint

• Endpoint: /alerts

• Method: GET

• Response:

  [
    {
      "alert_id": "uuid1",
      "timestamp": "2024-01-01T12:05:00Z",
      "message": "Unusual high entry rate detected."
    }
  ]
  

5. Tradeoffs

6. Other Approaches

• Person Detection: Alternatives include Faster R-CNN, SSD, or even simpler background subtraction techniques.
  • Pros: Simpler to implement and train, lower computational requirements.
  • Cons: Lower accuracy, less robust to changes in lighting and background.
• Tracking: Alternatives include Kalman filters, optical flow-based methods.
  • Pros: Less computationally intensive than DeepSORT.
  • Cons: Less robust to occlusions and changes in appearance.
• Prediction Model: Alternatives include simpler classifiers like logistic regression, SVMs, or decision trees.
  • Pros: Faster training and prediction, easier to interpret.
  • Cons: Lower accuracy, less able to capture complex temporal patterns.
• Data Storage: Alternatives include NoSQL databases like MongoDB.
  • Pros: More flexible schema, easier to scale horizontally.
  • Cons: Less mature ecosystem, may not support complex queries as efficiently.
• Hardware: Cloud-based processing.
  • Pros: Lower upfront cost, easier to manage.
  • Cons: Higher latency, requires more bandwidth, potential privacy concerns.

7. Edge Cases

• Multiple People: The system should accurately track and predict the entry behavior of multiple people approaching the door simultaneously.
  • Solution: The tracking module must be robust enough to handle crowded scenes. The prediction module can consider interactions between people.
• Occlusions: People may be partially or fully occluded by other objects or people.
  • Solution: The tracking module should use techniques like re-identification to maintain track of people even when they are occluded. The prediction module can use contextual information to infer entry intentions.
• Lighting Changes: Sudden changes in lighting can affect the performance of the detection and tracking modules.
  • Solution: The system should use adaptive image processing techniques to compensate for lighting changes. The models should be trained on data with diverse lighting conditions.
• Unusual Behavior: People may exhibit unusual behavior, such as loitering near the door or repeatedly approaching and retreating.
  • Solution: The system can use anomaly detection techniques to identify unusual behavior and flag it for further investigation. The prediction model can be adapted to learn from such behaviors.
• Camera Failure: The system should gracefully handle camera failures without interrupting overall functionality.
  • Solution: Redundant cameras can be deployed to provide backup coverage. The system should automatically switch to a backup camera if the primary camera fails.

8. Future Considerations

• Improved Accuracy: Continuously improve the accuracy of the prediction model by collecting more data and exploring more advanced machine learning techniques.
• Integration with Access Control: Integrate the system with access control systems to automatically unlock the door for authorized personnel.
• Personalized Predictions: Develop personalized prediction models that take into account individual preferences and habits.
• Expanded Sensor Integration: Integrate data from other sensors, such as proximity sensors and RFID readers, to improve the accuracy of the predictions.
• Scalability: Design the system to handle a large number of cameras and users.
• Privacy Enhancements: Implement privacy-preserving techniques to protect the identity of the people being tracked.
• Real-time Feedback Loop: Implement a real-time feedback loop to adapt the system to changing environmental conditions and user behavior.