Line Crossing Counter with AI on Raspberry Pi (Entry / Exit Detection)

Question

Line Crossing Counter with AI on Raspberry Pi

Hardware: Raspberry Pi 4 / 5
AI type: Computer Vision – Object Detection

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Overview

This project shows how a Raspberry Pi can count entries and exits using AI.
Objects are detected by a lightweight AI model and counted when they cross a virtual line.
Everything runs locally, without cloud services or GPUs.

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What you build

• AI object detection on Raspberry Pi

• Entry / exit counting using a virtual line

• Fully offline processing

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Required hardware

• Raspberry Pi 4 or 5

• Camera (Pi Camera or USB)

• microSD card

• Power supply

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Software

• Raspberry Pi OS

• Python 3

• OpenCV

• Ultralytics YOLO

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Installation

sudo apt update sudo apt upgrade sudo apt install python3-opencv python3-pip pip3 install ultralytics

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Concept

A horizontal virtual line is placed in the video frame.
When an object’s center crosses the line, it is counted as entry or exit depending on direction.

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Python code (copy-paste)

import cv2 from ultralytics import YOLO model = YOLO("yolov8n.pt") cap = cv2.VideoCapture(0) line_y = 240 prev_y = None entries = 0 exits = 0 while True: ret, frame = cap.read() if not ret: break results = model(frame, verbose=False) boxes = results[0].boxes if len(boxes) > 0: x1, y1, x2, y2 = boxes[0].xyxy[0] cy = int((y1 + y2) / 2) if prev_y is not None: if prev_y < line_y and cy >= line_y: entries += 1 elif prev_y > line_y and cy <= line_y: exits += 1 prev_y = cy cv2.line(frame, (0, line_y), (frame.shape[1], line_y), (255, 0, 0), 2) cv2.putText(frame, f"Entries: {entries}", (10, 30), cv2.FONT_HERSHEY_SIMPLEX, 0.8, (0,255,0), 2) cv2.putText(frame, f"Exits: {exits}", (10, 60), cv2.FONT_HERSHEY_SIMPLEX, 0.8, (0,0,255), 2) cv2.imshow("Line Counter", frame) if cv2.waitKey(1) & 0xFF == 27: break cap.release() cv2.destroyAllWindows()

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Applications

• People entry/exit counting

• Store traffic analysis

• Simple access monitoring

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Limitations

• Works best with one main object

• Occlusions reduce accuracy

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Conclusion

Raspberry Pi can perform practical AI analytics like entry and exit counting without expensive hardware.
This is a real, deployable AI use case on affordable devices.