ATP Core Talent 2025

Core Talent AI Coder Challenge: Camera Movement Detection

Detecting Significant Camera Movement Using Image Recognition

Scenario

Imagine you are tasked with building a component for a smart camera system. Your goal is to detect significant movement—for example, if someone moves or tilts the camera or if the entire camera is knocked or shifted. This is different from simply detecting moving objects in the scene.

Requirements

Input:
- A sequence of images or frames (at least 10-20), simulating a fixed camera, with some frames representing significant camera movement (tilt, pan, large translation), and others showing a static scene or minor background/object motion.
- You may use public datasets, generate synthetic data, or simulate with your own webcam.
  - Example: CameraBench Dataset on Hugging Face
Task:
- Build an algorithm (Python preferred) that analyzes consecutive frames and detects when significant camera movement occurs.
- Output a list of frames (by index/number) where significant movement is detected.
Expected Features:
- Basic: Frame differencing or feature matching to detect large global shifts (e.g., using OpenCV’s ORB/SIFT/SURF, optical flow, or homography).
- Bonus: Distinguish between camera movement and object movement within the scene (e.g., use keypoint matching, estimate transformation matrices, etc.).
Deployment:
- Wrap your solution in a small web app (Streamlit, Gradio, or Flask) that allows the user to upload a sequence of images (or a video), runs the detection, and displays the result.
- Deploy the app on a public platform (Vercel, Streamlit Cloud, Hugging Face Spaces, etc.)
Deliverables:
- Public app URL
- GitHub repo (with code and requirements.txt)
- README (explaining your approach, dataset, and how to use the app)
  - Sample README Outline:
    - Overview of your approach and movement detection logic
    - Any challenges or assumptions
    - How to run the app locally
    - Link to the live app
    - Example input/output screenshots
- AI Prompts or Chat History (if used for support)

Evaluation Rubric

Criteria	Points	Details
Correctness	5	Accurately detects significant camera movement; low false positives/negatives.
Implementation	5	Clean code, good use of OpenCV or relevant libraries, modular structure.
Deployment	5	App is online, easy to use, and functions as described.
Innovation	3	Advanced techniques (feature matching, transformation estimation, clear object vs camera).
Documentation	2	Clear README, instructions, and concise explanation of method/logic.

Suggested Stack

Python or C#
OpenCV for computer vision
Streamlit, Gradio, or a shadcn-powered Vercel site for quick web UI
GitHub for code repo, Streamlit Cloud, Hugging Face Spaces, or Vercel for deployment

📋 Candidate Instructions

Fork this repository (or start your own repository with the same structure).
Implement your movement detection algorithm in movement_detector.py.
Develop a simple web app (app.py) that allows users to upload images/sequences and view detection results.
Deploy your app on a public platform (e.g., Streamlit Cloud, Hugging Face Spaces, Vercel, Heroku) and share both your deployed app URL and GitHub repository link.
Document your work: Include a README.md that explains your approach, how to run your code, and sample results (with screenshots or example outputs).

Deadline: 🕓 27.06.2025

Plagiarism Policy:

This must be individual, AI-powered work.
You may use open-source libraries, but you must cite all external resources and code snippets.
Do not submit work copied from others or from the internet without proper acknowledgment.

Good luck! Show us your best hands-on AI skills!