Advanced Motion Analysis for Eye-Tracking

@ZohaibRoy

⭐⭐⭐⭐⭐ Create a Reliable Motion Analysis Engine for Eye-Tracking Videos ❇️ Hi My Friend, I hope you're doing well. I've reviewed your project requirements and see you're looking for a Computer Vision expert to implement a motion analysis engine. You don’t need to look any further; Zohaib is here to help you! My team has successfully completed 50+ similar projects in motion analysis and eye-tracking. I will use frameworks like OpenCV and MediaPipe to build a reliable server-side script that processes your video uploads automatically. ➡️ Why Me? I can easily create your motion analysis engine as I have 5 years of experience in Computer Vision and Digital Signal Processing. My expertise includes object tracking, noise filtering, and mathematical modeling. Additionally, I have a strong grip on technologies like Python, OpenCV, and MediaPipe, ensuring a thorough approach to your project. ➡️ Let's have a quick chat to discuss your project in detail. I would love to show you samples of my previous work. Looking forward to discussing this with you! ➡️ Skills & Experience: ✅ Python Programming ✅ OpenCV ✅ MediaPipe ✅ Digital Signal Processing ✅ Object Tracking ✅ Noise Filtering ✅ Data Analysis ✅ JSON Output Creation ✅ Video Processing ✅ Pattern Classification ✅ Mathematical Modeling ✅ Algorithm Development Waiting for your response! Best Regards, Zohaib

@opencollar

Hi, I see that accurately tracking eye movements and filtering out noise is key for your platform. I will use open-source tools like MediaPipe Iris to reliably find the eye center points and clean the data from blinks or sudden shifts. For the movement metrics, I will analyze the coordinates to find how fast or frequently the eye moves, and classify different motion types easily. I will make sure all data is neatly stored in JSON, ready for your API to read. I promise clear updates, good quality work, and will help save you time while supporting your project’s growth. Let’s talk about your plan and craft something even better together. Regards, Nick.

@sajjadtaghvaeifr

Hello, I trust you're doing well. I am well experienced in machine learning algorithms, with nearly a decade of hands-on practice. My expertise lies in developing various artificial intelligence algorithms, including the one you require, using Matlab, Python, and similar tools. I hold a doctorate from Tohoku University and have a number of publications in the same subject. My portfolio, which showcases my past work, is available for your review. Your project piqued my interest, and I would be delighted to be part of it. Let's connect to discuss in detail. Warm regards. please check my portfolio link: https://www.freelancer.com/u/sajjadtaghvaeifr

@kamran2012

I can help with this, I will build the server-side motion analysis engine — pupil tracking with MediaPipe Iris, blink/noise filtering, DSP-based metric extraction (frequency, amplitude, velocity), and pattern classification outputting structured JSON. For the frequency analysis, I will apply an FFT on the pupil coordinate time series to extract dominant oscillation frequencies, then use windowed velocity thresholds to distinguish linear shifting from acceleration phases — this approach handles variable frame rates more reliably than fixed-interval sampling. Questions: 1) What is the typical frame rate and resolution of the uploaded eye-strip videos? 2) Should torsional movement detection rely on iris texture rotation tracking, or is a simpler heuristic acceptable for the initial version? Looking forward to your response. Best regards, Kamran

@Herculesz

Hi I can build the Python server-side motion analysis engine to process uploaded eye-strip videos frame-by-frame and return clean tracking metrics as structured JSON. I have experience with Python, OpenCV, MediaPipe, computer vision tracking, digital signal processing, NumPy/SciPy, video frame analysis, noise filtering, coordinate extraction, and backend automation workflows. The main technical challenge is keeping pupil tracking reliable despite blinks, eyelashes, lighting changes, and small camera movement artifacts. I will solve this by combining iris/pupil localization, contour validation, blink-frame filtering, smoothing, outlier removal, and calibrated coordinate tracking. I can then calculate frequency, amplitude, velocity, acceleration phases, and movement direction from the X/Y coordinate arrays. The output can include raw coordinates, filtered coordinates, confidence scores, detected pattern type, movement metrics, and frequency-domain data in API-ready JSON format. My focus will be a reliable, modular analysis script that integrates cleanly with your existing upload backend. Thanks, Hercules

@corpmember29

HELLO, I HAVE CAREFULLY REVIEWED YOUR REQUIREMENTS AND UNDERSTAND THAT YOU NEED A SERVER-SIDE AUTOMATED EYE-TRACKING MOTION ANALYSIS ENGINE CAPABLE OF PROCESSING CROPPED EYE VIDEOS, TRACKING PUPIL MOVEMENT, APPLYING DIGITAL SIGNAL PROCESSING TECHNIQUES, CLASSIFYING MOVEMENT PATTERNS, AND EXPORTING STRUCTURED JSON OUTPUT FOR API CONSUMPTION. WITH 10+ YEARS OF EXPERIENCE IN COMPUTER VISION, PYTHON, OPENCV, MEDIAPIPE, MACHINE LEARNING, SIGNAL PROCESSING, AND DATA ANALYTICS, I CAN DELIVER A ROBUST AND SCALABLE SOLUTION INTEGRATED WITH YOUR EXISTING BACKEND WORKFLOW. KEY DELIVERABLES: • Automated Video Processing Trigger on Upload • Accurate Pupil Detection & Tracking (X,Y Coordinates) • Blink, Eyelash & Noise Artifact Filtering • OpenCV / MediaPipe Iris Integration • Frequency, Amplitude & Velocity Analysis • Motion Pattern Classification (Horizontal, Vertical, Torsional) • Linear Movement & Acceleration Detection • Frame-by-Frame Signal Processing Pipeline • Structured JSON Export for API Integration • Optimized & Scalable Server-Side Python Implementation I WILL PROVIDE 2 YEARS OF FREE ONGOING SUPPORT, COMPLETE SOURCE CODE, AND FULL ASSISTANCE FROM DEVELOPMENT TO PRODUCTION DEPLOYMENT. I EAGERLY AWAIT YOUR POSITIVE RESPONSE. THANKS

@devendrathakur12

Hello, I would love if I get the chance to work on your project. I built many complex pipelines in the past and I enjoy building reliable analysis pipelines with Python, OpenCV, MediaPipe Iris, NumPy, and SciPy, where accuracy and reproducibility matter more than shortcuts. My focus would be producing stable pupil tracking, robust noise filtering, and clean JSON output that integrates smoothly with your existing backend. One question I have is, will the uploaded eye strip videos always have a fixed frame rate and resolution, or should the processing pipeline normalize recordings before extracting DSP metrics? Can we connect over a chat to discuss more about the project? Best regards, Dev Singh

@C0RETECHIES

Hi there, I understand the workflow: an uploaded eye video triggers a server-side script. This script tracks the pupil's (X,Y) coordinates, filters out noise like blinks, applies DSP to compute metrics (frequency, velocity), classifies the movement, and exports a structured JSON for your API. Technical approach: A Python script using MediaPipe Iris for robust tracking. NumPy and SciPy will handle signal processing for metric extraction (FFT, derivatives). A Kalman filter will smooth the trajectory and handle data gaps from blinks, ensuring clean data for analysis. Core modules: - Automated Video Ingestion - Pupil Trajectory Generation - Signal Filtering & Smoothing - Motion Metrics Calculation - Pattern Classification Engine - JSON Output Formatter I'll first build and validate the core CV/DSP logic on sample videos. Once the tracking and metrics are accurate, I'll integrate the script with a server-side trigger for a production-ready solution. Questions: 1. For torsional movement, is pupil path curvature analysis sufficient, or is true iris rotation tracking required? 2. Is velocity in 'pixels/second' acceptable, or is a calibration process available to get 'degrees/second'? 3. What is the expected video resolution and average processing load (videos/hour)? Regards, Rohit

@nrajuu07

I'm a computer vision and digital signal processing specialist experienced in real-time eye-tracking and motion analysis systems. I'll build a production-grade Python backend script leveraging MediaPipe Iris and OpenCV for robust pupil tracking — filtering blinks, eyelashes, and camera artifacts — and apply DSP techniques to extract frequency, amplitude, and velocity metrics from coordinate arrays. Pattern classification will identify horizontal, vertical, and torsional movements with acceleration phase detection. All outputs exported as clean, structured JSON ready for your API consumption. Fully automated on video upload with comprehensive error handling and logging. Ready to start immediately.

@seefattechnologi

Advanced Motion Analysis for Eye-Tracking--------Experienced in Python, OpenCV, MediaPipe, object tracking, digital signal processing, and time-series analysis. I focus on accurate tracking, noise filtering, blink handling, and reliable metric extraction to deliver precise and scalable analysis results. Please ping me to discuss further and deliver exceptional results. Thanks!!!

@mobimubasir

i can build your server side python engine using mediapipe iris or opencv to track pupil x y coordinates frame by frame filter out blinks eyelashes and camera jitter then apply dsp to extract frequency amplitude and velocity plus classify horizontal vertical torsional patterns and linear vs acceleration phases. script triggers automatically on upload and exports clean json with metrics coordinate arrays and frequency data ready for your api. send a sample eye strip video and api spec then i will share timeline 4 to 5 days approach and test results so we can validate accuracy before integration. bhej diya yaar

@amirrmusavifr

Hi, I’m an AI expert with professional experience in computer vision, with a proven track record of working on complex image processing and AI/ML model development. With skill sets: • Algorithm Development: Strong understanding of computer vision algorithms and techniques, including convolutional neural networks (CNNs), object detection, image segmentation and feature extraction. • Model Training & fine-tuning: Develop and train machine learning models tailored for image analysis and visual data interpretation. I have worked on some well-known models like YOLO, RCNN, U-Net, Deeplab, ViT etc. • AI Integration: Implement and integrate AI models into existing software and hardware systems, ensuring high performance and scalability. • Data Analysis: Analyze and process large datasets of images and video feeds to identify patterns, trends, and insights. • Data Handling: Experience in handling and processing large datasets, including image and video data. Familiarity with data augmentation techniques and synthetic data generation. • Performance Optimization: Optimize algorithms and models for real-time processing and ensure they can handle large-scale data efficiently. • Programming Skills: Proficient in programming languages such as Python. Experience with deep learning frameworks like TensorFlow, PyTorch, or Keras. • Tools & Libraries: Proficiency with OpenCV, scikit-image, and other relevant libraries. Experience with version control systems like Git.

@GdevDataSceince

Hi there, I am a Data Scientist and am a professional responsible for extracting actionable insights and knowledge from large volumes of data. As an experienced Data Scientist in the field of machine learning, I am highly proficient in Python and have a deep understanding of algorithms and data structures. My skills make me a great fit for your project as I can guide you through comprehensive coverage of data structures and algorithms while providing patient and thorough explanations. I have over 12-plus years of experience with Python Library Pandas, Karas, TensorFlow, NumPy, PyCharm, Py torch, Open CV, NLP, and others. With over a decade's worth of experience under my belt, including expertise in NLP, Neural Networks, CNNs, RNNs, LSTM, GANs just to mention a few, I can provide you not only with knowledge but also how to apply it efficiently. Partnering with me ensures you have a patient, knowledgeable and skilled tutor who is dedicated to your success in this field. My top priority is to provide a high quality of work, https://www.freelancer.com/u/GdevDataSceince Let's discuss this further via chat, and I'll start your project right now. Thanks Gdev

@rosscarter1011

Hey there, The noise filtering step is where this kind of pipeline gets tricky — blinks and eyelash artifacts can completely throw off the frequency and amplitude calculations if they're not caught cleanly before the DSP layer runs. I'd use MediaPipe Iris for the pupil coordinate extraction, apply a Savitzky-Golay or Butterworth filter to smooth the signal, then run the FFT-based frequency analysis and velocity calculations before classifying the movement pattern and exporting the clean JSON. Built server-side computer vision pipelines with OpenCV and MediaPipe before. Happy to dig into the technical approach here in the messages!

@vishal1145

Quick question - are you processing these eye-strip videos in real-time or batch mode? If you're expecting sub-500ms latency per video, we'll need GPU acceleration and a different pipeline than if you're running overnight batch jobs. Second - what's your current server infrastructure? If you're on AWS Lambda with 15-minute timeouts, we'll hit limits on longer videos and need to chunk processing differently than if you're running dedicated EC2 instances. Your core challenge isn't just tracking the pupil - it's handling the edge cases that break most CV pipelines. Partial occlusions from eyelids during micro-blinks will corrupt your frequency analysis if you're not interpolating missing frames correctly. Camera shake will introduce false positives in your velocity calculations unless you implement background stabilization first. Here's the technical approach: - MEDIAPIPE IRIS + KALMAN FILTERING: Track pupil center with sub-pixel accuracy while predicting positions during blink frames to maintain continuous coordinate streams for FFT analysis. - SIGNAL PROCESSING PIPELINE: Apply bandpass filtering (0.5-5Hz typical for nystagmus) on coordinate arrays, then run FFT to extract dominant frequencies and calculate peak-to-peak amplitude in degrees using known eye geometry. - MOTION CLASSIFICATION: Build a decision tree based on X/Y velocity ratios and acceleration profiles to distinguish horizontal saccades from smooth pursuit vs torsional rotation patterns. - OPENCV BACKGROUND SUBTRACTION: Implement frame differencing to detect and compensate for camera movement before calculating pupil displacement vectors. - JSON SCHEMA: Structure output with nested arrays for raw coordinates, filtered signals, frequency spectrum data, and classified movement segments with confidence scores. I've built similar CV pipelines for medical imaging clients processing 10K+ videos daily. The difference between a prototype that works on clean test data and production code that handles real-world noise is proper signal conditioning and fallback logic when tracking fails. Let's schedule a 15-minute call to review your sample videos and server specs before I architect the processing pipeline.

@talkwaqar

Two details I keyed on: the upload trigger that has to fire automatically on each cropped eye-strip video, and the need to classify movement as Horizontal, Vertical, or Torsional from the pupil coordinate arrays. The hard part isn't the tracking, it's keeping the (X, Y) signal clean enough that blinks and eyelash occlusion don't poison your frequency math. Week one I'd ship the tracking core plus a sample JSON so you can wire your API against a real schema early. How I'd build it: 1. MediaPipe Iris for per-frame pupil center, with a blink gate (eye-aspect-ratio threshold) and a median plus low-pass filter to drop eyelash and camera-jitter spikes. 2. DSP on the cleaned arrays: FFT for dominant frequency, peak-to-peak for amplitude, and frame-delta for velocity in pixels and degrees per second. 3. Classify axis (H/V/torsional) from the X vs Y variance ratio and detect linear-shift vs acceleration phases from the velocity derivative. I build Python data pipelines that output strict JSON for downstream APIs, so the contract your core API reads stays stable across edge cases. Done = upload a test video, get back a JSON with coordinate arrays, frequency, amplitude, velocity, and pattern class, no manual step. €1,200, 7 days. One question to scope it right: do you have 5 to 10 sample eye-strip videos I can tune the blink and noise thresholds against, or should I build a synthetic test set first? P.S. MediaPipe Iris gives a normalized iris landmark, but torsional (rotational) motion barely moves the pupil center. I'd track an iris texture feature or the limbus angle for the torsional case, otherwise that class stays near silent in the output. Waqar

@indiainfosoft

With my extensive expertise in Computer Vision and Digital Signal Processing, I am confident that I am the perfect fit for your advanced motion analysis project. I understand the value of using open-source frameworks such as OpenCV contour tracking and MediaPipe Iris, and have worked extensively with them in similar projects involving eye-tracking. My 18+ years of experience has instilled in me an acute attention to detail that will be instrumental in tackling the most challenging aspects of this task. Noise filtering is crucial in any vision-based analysis and I have successfully dealt with removing blinks, eyelashes, and camera movement artifacts in previous projects. More importantly, employing digital signal processing techniques for accurate frequency, amplitude, and speed calculation is a specialty of mine. Additionally, my ability to effectively classify movement patterns such as horizontal, vertical, and torsional will aid in discerning motion behaviors - linear shifting or acceleration phases. Lastly, I consistently deliver clean outputs that adhere to industry best practices. Your JSON files will be structured and well-documented for easy integration with your core API. My skills combined with my dedication to providing quality work make me not only an ideal fit technically but also professionally; ensuring open communication and efficient output throughout the project.

@alizain721

With cropped eye-strip uploads the real risk is that blinks, eyelashes and tiny camera drift create false peaks that break frequency and amplitude estimates. You already have the frontend upload and need a backend that reliably ignores those artifacts and emits clean metrics — that is the problem I will solve. Planned approach: build a Python server-side processor that triggers on upload, using MediaPipe Iris as the primary pupil tracker with an OpenCV contour-based fallback. Preprocessing will include temporal stabilization, blink and occlusion detection, and an adaptive noise filter so small camera movement does not corrupt the coordinate stream. For metrics I will apply DSP methods — bandpass filtering, FFT and instantaneous amplitude/speed via numerical differentiation and Hilbert transform when appropriate — to produce frequency, amplitude and velocity (pixels/sec). Pattern classification will use engineered features and a lightweight classifier to label Horizontal, Vertical or Torsional motion and flag linear versus accelerating phases. Final output will be a clean JSON with timestamps, coordinate arrays, frequency bands, metric summaries and diagnostics ready for your API. Relevant proof: on Tranero I built the backend event-driven ETL and robust retry pipelines that convert incoming files into structured outputs and logs. That project taught the reliability and observability patterns I will apply here to video processing and JSON export. My bid: 1125 EUR. Estimated delivery for a first production-ready pipeline: 10–14 days after access and sample data. Could you share two representative eye-strip videos (one with prominent blinks), the upload storage endpoint or repo/staging access, and the recorded frame rate so I can wire the automatic trigger and calibrate velocity units?

@demivision

Hi there, Thank you for sharing the detailed requirements for your Advanced Motion Analysis for Eye-Tracking project. We are DemiVision LLC, a team specializing in computer vision, machine learning, and digital signal processing solutions, with extensive experience building robust server-side analytics engines for video and biometric data. We fully understand the importance of precise, automated eye movement analysis for your platform’s backend. Your need to process "eye-strip" videos, extract reliable pupil coordinates, filter out noise (blinks, eyelashes, camera shake), and generate actionable metrics aligns perfectly with our expertise. We have successfully developed similar pipelines using Python, leveraging OpenCV, MediaPipe Iris, and custom digital signal processing algorithms to deliver high-accuracy tracking and behavioral classification. Our approach will integrate the best open-source frameworks to robustly isolate and track the pupil per frame, followed by advanced noise reduction routines to ensure clean and reliable data. We will apply mathematical modeling and signal processing to extract frequency, amplitude, and velocity metrics from the coordinate time-series, enabling both quantitative analysis and qualitative movement classification (horizontal, vertical, torsional, linear, acceleration, etc.). All extracted metrics and raw coordinate arrays will be structured into a clean, well-documented JSON output, fully compatible with your core API for seamless downstream integration. We are passionate about delivering scalable, reliable server-side solutions and would be excited to contribute to your innovative data analysis platform. Let’s discuss how DemiVision LLC can help bring your vision to life with precision and efficiency. Looking forward to your response! Best regards, The DemiVision LLC Team