Running AI on the Frontend: Pose Estimation with TensorFlow Lite MoveNet

1. Front‑end Can Run AI

TensorFlow Lite provides a lightweight Lite runtime that supports Android, iOS, and Raspberry Pi, allowing AI models to execute directly on user devices. Web developers can also use TensorFlow.js to run the same models in browsers.

2. Pose Estimation Demo

The article demonstrates a pose‑estimation example using TensorFlow’s MoveNet models. MoveNet offers three families: Thunder (high accuracy, slower), Lightning (lightweight, fast), and Multipose (multiple people detection).

2.1 Getting Started with the Android Example

After downloading the official Android example from TensorFlow Lite Pose Estimation , open the README.md to see the environment requirements (Android Studio 4.2+, SDK API 21) and build steps.

2.2 Working Principle

The same MoveNet model runs on Android, iOS, Raspberry Pi, and browsers. It accepts either live camera frames or static images and outputs 17 body‑keypoints (nose, eyes, ears, shoulders, elbows, wrists, hips, knees, ankles) with confidence scores.

Keypoint output example (Python representation):

Person(
    bounding_box=Rectangle(start_point=Point(x=140, y=72), end_point=Point(x=262, y=574)),
    score=0.61832184,
    keypoints=[
        KeyPoint(body_part=BodyPart.NOSE, coordinate=Point(x=193, y=85), score=0.5537756),
        ...
        KeyPoint(body_part=BodyPart.RIGHT_ANKLE, coordinate=Point(x=150, y=574), score=0.83522)
    ]
)

2.3 Code Walkthrough

A minimal Python project is provided (GitHub: hlwgy/juejin_movenet ) with the following structure:

tflite model files
  ├─ movenet_lightning.tflite   # fast, lightweight
  ├─ movenet_thunder.tflite     # accurate, slower
  ├─ classifier.tflite          # yoga‑pose classifier
  └─ labels.txt                # class names

data.py          # data definitions
classifier.py    # classification utilities
movenet.py       # pose‑estimation wrapper
main.py          # demo entry point
test.jpeg        # sample image

Required environment: Python 3.8, numpy , opencv‑python (install via pip install numpy and pip install opencv-python ).

Example code to extract pose data from an image:

import cv2
from movenet import Movenet

pose_detector = Movenet('tflite/movenet_thunder')
input_image = cv2.imread('test.jpeg')
person = pose_detector.detect(input_image)
print(person)

Drawing the results on the image:

bounding_box = person.bounding_box
keypoints = person.keypoints
cv2.rectangle(input_image, bounding_box.start_point, bounding_box.end_point, (255, 0, 0), 2)
for kp in keypoints:
    cv2.circle(input_image, kp.coordinate, 2, (0, 0, 255), 4)
cv2.imwrite('output.png', input_image)

Real‑time camera loop (simplified):

cap = cv2.VideoCapture(0)
while cap.isOpened():
    success, frame = cap.read()
    frame = cv2.flip(frame, 1)
    person = pose_detector.detect(frame)
    # ...process and display...

2.3.2 Applying Pose Data

The 17 keypoints can be fed into a classifier model to recognize actions such as yoga poses. Using the provided classifier.tflite , the sample image test.jpeg is classified as the "tree" pose.

Developers can train their own TensorFlow models for custom actions and integrate the results into interactive applications (e.g., animating avatars based on user movements).

3. Source Code, Models and Resources

The repository includes Python code, TensorFlow Lite model files for Lightning, Thunder, and Multipose, as well as Android and iOS source projects and a pre‑built tf_app-release.apk for quick testing.

4. References

Official TensorFlow Lite pose‑estimation guide, TensorFlow.js pose‑detection blog, and the GitHub example repository are listed for further reading.