Advanced Motion Analysis for Eye-Tracking
Budget: €750 – €1,500 EUR
Project Overview:
We are building a data analysis platform and require a Computer Vision / Digital Signal Processing expert to implement a server-side automated motion analysis engine. The frontend application layer is already complete and successfully uploads a cropped "eye-strip" video (focusing strictly on the eye region) directly to our server backend.
Your Task:
you will leverage existing open-source frameworks (such as OpenCV contour tracking, MediaPipe Iris, or similar established eye-tracking libraries) to build a highly reliable server-side script (Python preferred). This script must trigger automatically upon video upload to process the cropped video frame-by-frame.
Core Technical Deliverables:
1. Object Tracking & Noise Filtering: Utilize MediaPipe Iris or OpenCV to isolate and track the center coordinates (X, Y) of the pupil dynamically. The algorithm must successfully filter out blinks, eyelashes, and minor camera movement artifacts.
2. Mathematical Metrics Extraction: Apply digital signal processing / mathematical modeling to calculate the frequency, amplitude, and speed (velocity in pixels or degrees per second) of the repetitive tracking movements based on the coordinate arrays.
3. Pattern Classification: Interpret the generated positional vectors to classify the movement patterns (Horizontal, Vertical, Torsional) and detect motion behaviors (linear shifting vs. acceleration phases).
4. Clean JSON Output: Export all mathematical metrics, coordinate arrays, and frequency data into a structured JSON file, ready to be read by our core API.
We are building a data analysis platform and require a Computer Vision / Digital Signal Processing expert to implement a server-side automated motion analysis engine. The frontend application layer is already complete and successfully uploads a cropped "eye-strip" video (focusing strictly on the eye region) directly to our server backend.
Your Task:
you will leverage existing open-source frameworks (such as OpenCV contour tracking, MediaPipe Iris, or similar established eye-tracking libraries) to build a highly reliable server-side script (Python preferred). This script must trigger automatically upon video upload to process the cropped video frame-by-frame.
Core Technical Deliverables:
1. Object Tracking & Noise Filtering: Utilize MediaPipe Iris or OpenCV to isolate and track the center coordinates (X, Y) of the pupil dynamically. The algorithm must successfully filter out blinks, eyelashes, and minor camera movement artifacts.
2. Mathematical Metrics Extraction: Apply digital signal processing / mathematical modeling to calculate the frequency, amplitude, and speed (velocity in pixels or degrees per second) of the repetitive tracking movements based on the coordinate arrays.
3. Pattern Classification: Interpret the generated positional vectors to classify the movement patterns (Horizontal, Vertical, Torsional) and detect motion behaviors (linear shifting vs. acceleration phases).
4. Clean JSON Output: Export all mathematical metrics, coordinate arrays, and frequency data into a structured JSON file, ready to be read by our core API.