How to map people detected in a fixed camera view to a 2D seat layout (seat occupancy, not person ID)?

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Summary

The problem of mapping people detected in a fixed camera view to a 2D seat layout is a spatial mapping and assignment problem. The goal is to determine whether a given seat is occupied or not, without identifying the person. The current approach uses object detection (e.g., YOLO) and region of interest (ROI) to define a fixed area for each seat.

Root Cause

The main challenge is reliably mapping people detected in a 3D camera view to a 2D abstract seat layout. This requires:

  • Accurate detection of people in the camera view
  • Robust mapping of detected people to seats
  • Handling occlusion and missed detections

Why This Happens in Real Systems

In real-world systems, variations in camera angles, lighting conditions, and student movements can affect the accuracy of people detection and seat mapping. Additionally, temporary occlusion and missed detections can occur due to:

  • Students standing up or leaning forward
  • Other students or objects blocking the camera view
  • Poor lighting or shadows

Real-World Impact

The impact of inaccurate seat mapping can be significant, including:

  • Inaccurate attendance records
  • Incorrect student tracking
  • Difficulty in identifying empty seats

Example or Code

import cv2
import numpy as np

# Define ROI for each seat
seat_rois = [
    np.array([[100, 100], [200, 100], [200, 200], [100, 200]]),
    np.array([[300, 100], [400, 100], [400, 200], [300, 200]])
]

# Detect people using YOLO
net = cv2.dnn.readNet("yolov3.weights", "yolov3.cfg")
cap = cv2.VideoCapture(0)

while True:
    ret, frame = cap.read()
    if not ret:
        break

    # Get detections
    detections = net.forward(frame)

    # Map detections to seats
    for detection in detections:
        x, y, w, h = detection[0:4]
        center_x = x + w / 2
        center_y = y + h / 2

        for seat_roi in seat_rois:
            if cv2.pointPolygonTest(seat_roi, (center_x, center_y), False) >= 0:
                # Seat is occupied
                print("Seat occupied")
                break

How Senior Engineers Fix It

Senior engineers use camera calibration and homography to improve the accuracy of seat mapping. They also implement:

  • Time windows and hysteresis to handle temporary occlusion and missed detections
  • Seat-centric design to maintain the state of each seat
  • Person-centric tracking to assign people to seats

Why Juniors Miss It

Juniors may miss the importance of:

  • Accurate camera calibration
  • Robust mapping algorithms
  • Handling occlusion and missed detections
  • Choosing the right system design (seat-centric vs person-centric)