Computer Vision Developer

@shrec

Hello, I’m an experienced computer vision developer (OpenCV, Python) and have built tracking systems for noisy industrial video environments. For your project, I would implement a hybrid approach: ArUco marker tracking for stable detection Optical flow / feature tracking as fallback comparison Frame-by-frame motion analysis with smoothing for reflections and low-quality video Outputs: annotated video, CSV data export, and movement-over-time chart I’ve worked on similar video-based measurement and tracking systems, including industrial use cases with variable lighting. Timeline: ~7–10 days for prototype Approach: quick MVP → compare methods → optimize accuracy Happy to build a reliable replacement for your legacy system. Best regards, Ivane

@irsolutions

With a strong background in computer vision and image processing, I understand the importance of building a modern and automated system to track pallet movement from video recordings for your load stability test project. Having successfully scaled systems to serve over 1 million users, I am confident in tackling the challenges of automating this process to replace the outdated manual tracking solution you currently have in place. One strategic insight I would recommend is implementing a hybrid tracking approach using both natural visual points and ArUco markers to ensure stable and reliable tracking, especially considering the varying lighting conditions and reflections that may occur during the tests. My experience in object tracking and handling noisy video, combined with my expertise in OpenCV and Python, positions me well to develop a robust solution for your tracking needs. I invite you to reach out so we can discuss the roadmap for this project in more detail. I am eager to collaborate on delivering a working prototype, along with clear instructions and potentially a user interface to enhance the functionality of the system. Let's explore the possibilities of this project and how we can achieve your goals together.

@diektech

Hi, This is Elias from Miami. I checked your project description and understand you’re looking to develop a system for automatic tracking of pallet movement from video using computer vision techniques. This involves leveraging technologies like OpenCV and Python to accurately process and analyze video footage. I have experience in building similar systems that focus on image processing and automation, which gives me insight into the technical challenges involved. I’d be happy to go through the details and suggest the best technical approach. I have a few questions to get a better understanding: Q1 – What specific video sources or formats will we be working with? Q2 – Are there any existing systems you’d like to integrate with for this tracking solution? Q3 – What are your expectations regarding the scalability and performance of the tracking system? Looking forward to hearing from you.

@LiveExperts

Hi, I can build this pallet tracking prototype using OpenCV with a focus on reliable measurement rather than just visual detection. I have experience with computer vision pipelines for object tracking noisy video marker detection and measurement style outputs. For this project I would first evaluate two approaches as requested. One using natural visual points on the pallet and one using ArUco or similar markers. This will show which method is more stable under reflections low video quality and changing lighting. The system will import video files detect and track the three pallet points frame by frame and calculate angular deviation from the 90 degree reference lateral displacement and maximum movement during the test. I will also generate an output video with tracking overlays reference line and marker paths along with CSV export and simple movement plots over time. I would likely use Python and OpenCV for the first prototype because it allows fast testing and clear handover. If needed the same logic can later be optimized or moved toward C++. Deliverables will include working prototype source code setup instructions and a clear explanation of the chosen tracking method. Happy to review sample videos and start with a practical prototype.

@ZohaibRoy

⭐⭐⭐⭐⭐ Automate Pallet Movement Tracking with Computer Vision ❇️ Hi My Friend, I hope you're doing well. I've reviewed your project needs and see you're looking for a computer vision developer. You don’t need to look any further; Zohaib is here to help you! My team has successfully completed 50+ similar projects in computer vision. I will create a modern system to track pallet movements using video recordings. We will ensure it works well even with low-quality videos and reflections. ➡️ Why Me? I can easily do your project as I have 5 years of experience in computer vision, specializing in object tracking and video analysis. My expertise includes OpenCV, image processing, and handling various video qualities. Additionally, I have a strong grip on Python and C++, ensuring a reliable solution for your needs. ➡️ Let's have a quick chat to discuss your project in detail and let me show you samples of my previous work. I look forward to discussing this with you in our chat. ➡️ Skills & Experience: ✅ OpenCV ✅ Image Processing ✅ Object Tracking ✅ Video Analysis ✅ Python ✅ C++ ✅ Data Exporting ✅ Angular Deviation Calculation ✅ Movement Visualization ✅ Frame-by-Frame Tracking ✅ Handling Noisy Videos ✅ User Interface Design (optional) Waiting for your response! Best Regards, Zohaib

@tangramua

Hello! We have solid hands-on experience in computer vision projects using OpenCV with both Python and C++, including object tracking, optical flow, feature detection and working with noisy, low-quality industrial video. We’ve built measurement and tracking solutions where reflections, unstable lighting and imperfect footage were key challenges, so this pallet movement task is very familiar in nature. For this system, we would prototype two tracking approaches in parallel: natural feature tracking and marker-based tracking using ArUco, then compare stability and accuracy under your real test conditions. From the tracked points, we calculate angular deviation from the 90° reference, lateral displacement and maximum movement and output a processed video with overlays plus CSV data and movement graphs over time. We deliver clean, well-structured source code, clear usage instructions, and can optionally add a lightweight UI for easier operation. Happy to share relevant computer vision examples and outline a detailed technical approach, timeline and budget after reviewing a sample video from your test setup. Please, review our profile https://www.freelancer.com/u/tangramua where you can find detailed information about our company, our portfolio, and the client's recent reviews. Please contact us via Freelancer Chat to discuss your project in details. Best regards, Kateryna Sales department Tangram Canada Inc.

@kamran2012

I can help with this, I will build the pallet tracking system — marker detection, frame-by-frame movement analysis, and data export with annotated video output. The prototype will compare both natural feature tracking and ArUco marker tracking so we can determine which approach holds up best under your conditions. For handling stretch film reflections, I will apply adaptive histogram equalization and temporal smoothing before the tracking stage. This reduces false detections caused by specular highlights that shift between frames — a common failure point with standard thresholding on reflective surfaces. For the angular and displacement calculations, I will establish a calibrated reference frame from the fixed camera position to convert pixel drift into millimeter values. Looking forward to talking through the details. Kamran

@teodorgeorge

Hello, I'm excited to support your project and build an automated system that tracks pallet movement accurately even with reflections and lower video quality. My background in OpenCV and feature tracking helps me create simple and effective solutions for industrial setups. I’ve worked on similar video analysis tasks and can deliver a clean prototype comparing natural points and ArUco markers, along with the processed video and data export you need. Thanks, Teo

@Herculesz

Hi I can build a robust computer vision system to automatically track pallet movement and replace your current manual process. A key challenge here is maintaining stable tracking under low-quality video, reflections from stretch film, and inconsistent lighting. I would approach this using Python with OpenCV, combining feature-based tracking (Lucas-Kanade optical flow) with fallback detection methods like blob detection and contour analysis. For higher reliability, I’ll prototype both natural feature tracking and ArUco marker-based tracking, then compare accuracy, drift, and robustness. The system will compute angular deviation from vertical using geometric transformations, measure lateral displacement, and export structured CSV data along with an annotated output video. I’ll also include noise handling techniques such as smoothing filters, outlier rejection, and frame stabilization to ensure consistent results across varying conditions. Thanks, Hercules

@corpmember29

ACCURATE, ROBUST PALLET MOTION TRACKING—REPLACING MANUAL ANALYSIS WITH AUTOMATION I understand you need a reliable CV system that tracks 3 pallet markers under noisy conditions and outputs precise movement metrics. With 12+ years in computer vision (OpenCV, tracking, industrial analysis), I’ve built similar measurement pipelines. Proposed Approach: Dual Strategy (as requested): ArUco markers → highest robustness (pose + angle directly) Markerless tracking → feature tracking (Lucas-Kanade optical flow + re-detection via ORB/SIFT) Stability: Kalman filtering + outlier rejection for reflections/stretch-film noise Calibration: Fixed camera → homography for pixel→mm conversion Angle & Displacement: Compute deviation from vertical + lateral shift + max excursion Low-quality handling: adaptive thresholding, contrast enhancement, ROI tracking Outputs: Video with overlays (points + reference line) CSV (frame, angle, displacement) Simple plot (movement vs time) Tech Stack: Python + OpenCV (+ NumPy, Matplotlib) Deliverables: ✔ Working prototype (ArUco vs markerless comparison) ✔ Clean, documented source code ✔ Usage guide

@Muhammadzeesha59

Hello Sir/MAM I am a skilled full stack developer. Having rich experience in Java , C++ , C , C# , Python , Eclipse , Sql , Mysql , .Net ,Oracle , Object Oriented Programming , Data Structure , Algorithms . I have a perfect grip on “Artificial Intelligence” “Automation” , and work in “Machine Learning” Deep Learning ”. My track record as demonstrated in my 100% job completion and 5-star review rating showcases My ability to deliver exceptional results on time and with utmost quality I believe that my skill set makes me the ideal candidate for this project Please come on chat we will discuss more about this I will be waiting for your reply . Thanks and Best Regards

@codefusion53

Hi This is a well-defined computer vision problem, but achieving reliable tracking under reflections and varying quality requires a thoughtful approach rather than a single method. I can build a robust tracking system using OpenCV that detects and tracks three pallet points frame by frame while handling noise and visual distortion. My approach would start with a prototype comparing feature-based tracking and ArUco markers to determine the most stable method under your conditions. I will implement smoothing, error correction, and fallback logic to ensure consistent tracking even with reflections from stretch film. From this, I will compute angular deviation, displacement, and maximum movement accurately. The final output will include processed video overlays, structured CSV data, and a simple visualization of movement over time. The solution will be clean, extensible, and ready for future scaling into a larger system. Best, Justin

@kareemmaize

Hi - the critical part here is not just tracking points, it is ensuring the measurement stays stable when reflections and low-quality frames distort what the camera sees. I’d approach this by testing both natural feature tracking and ArUco markers early, then locking the most reliable method. Video loads - frames are processed - 3 points detected - tracking follows frame-by-frame - angles and displacement are calculated - overlay and CSV are generated. Looks correct, but fails when reflections cause markers to disappear or drift mid-sequence. Tracking drift can corrupt measurements in real use, so I handle it with re-detection logic, smoothing filters, and confidence scoring. Input - video frames. Processing - tracking + calibration. Output - movement metrics. The part to get right early is the calibration and tracking stability.

@yuliussmayoru

OVER 10 YEARS OF EXPERIENCE IN IT, HIGH QUALITY DELIVERY Hey there! Tracking systems like this usually fail when markers drift, reflections confuse detection, or noisy video breaks consistency. I’d solve this with a robust tracking pipeline combining stable marker detection, fallback tracking, and frame-to-frame validation. I’ve spent over 10 years working on computer vision and video analysis systems, including tracking in imperfect conditions, so handling reflections, low-quality footage, and measurement accuracy is something I’m very comfortable with. For your case, I’d prototype two approaches: ArUco-based tracking for maximum stability and a feature/optical-flow-based method for markerless scenarios, then compare reliability and accuracy. I’d calculate angular deviation and displacement precisely by mapping tracked points to a fixed reference frame, and generate both overlay videos and clean CSV outputs with movement metrics. I also focus on making the system practical — stable across videos, easy to run, and well-documented. I work independently, communicate clearly, and aim for solutions that actually work in real-world conditions, not just ideal cases.

@maxscender

I can help you. The primary technical hurdle here isn't just tracking, but filtering out "pseudo-movement" caused by light reflecting off the stretch film, which often breaks standard optical flow or feature matching. I will implement a robust ArUco-based pipeline for the prototype because their internal parity bits are specifically designed to ignore the specular highlights and reflections that would otherwise cause "point drift" on a shiny surface. To solve the conversion from pixels to millimeters, I will use a Perspective-n-Point (PnP) solver to estimate the 3D pose of the pallet relative to the fixed camera, ensuring angular deviation is calculated in real-world space rather than skewed 2D screen space. For the natural point comparison, I’ll apply a Kalman Filter to the tracking coordinates to prevent frame-by-frame jitter common in low-quality video. This approach ensures that the "maximum movement" metric is based on actual pallet displacement rather than sensor noise or compression artifacts. My solution will include a sub-pixel interpolation layer to maintain high measurement precision even if the source video resolution is limited.

@chraheelbhatti

As a seasoned AI researcher and machine learning engineer with a Ph.D. in Ai & Machine Learning, I bring a unique blend of academic insight and practical expertise to your project. Over the past decade, my work has been focused heavily on developing and implementing advanced computer vision solutions that center around object tracking, image processing, and noise handling - precisely what your project demands. My wide-ranging experience across diverse sectors like voice recognition, surveillance, and automated workflows perfectly complements your requirements. What truly sets me apart is my long-term vision - not just for your project but more broadly for the evolution of our collaboration. This initial phase is undoubtedly important but it is just the beginning of what we can do together. Having spearheaded large-scale AI systems in my prior work at Unilever Pakistan and the State Bank of Pakistan, I am deeply attuned to working beyond isolated tasks; providing long-term value while addressing specific immediate needs. So let's embark on this transformative journey together by laying the strong foundation this project deserves!

@vladang

You’re replacing a decade-old manual pallet stability check with a vision system that can reliably track 3 markers through noisy industrial footage. I’ve worked on similar fixed-camera measurement setups where reflections and low-quality video are the main failure points. I’d build a Python OpenCV pipeline that detects either natural feature points or ArUco markers, then evaluates both for stability under real conditions. Tracking would combine frame-to-frame motion estimation with geometric validation to reduce drift from reflections or occlusion. From tracked coordinates, I’ll compute angular deviation vs a vertical reference and lateral displacement in pixels/mm, with max excursion per test. Outputs will include annotated video overlays plus CSV export for downstream analysis and simple trend visualization. The goal is a deterministic, repeatable measurement tool that behaves consistently across varying lighting and film reflections. Q1: ArUco primary or just for benchmarking natural tracking? Q2: What pixel/mm accuracy is acceptable for stability measurement? Q3: Should output prioritize engineering detail or operator simplicity?

@salesapple

With 8+ years in computer vision and OpenCV-based systems, I can build a highly accurate and robust pallet tracking solution that replaces your legacy workflow with a modern, automated pipeline. I’ll develop a prototype in Python (or C++ if preferred) that processes video, detects and tracks three key points using a hybrid approach (feature tracking + optical flow + optional ArUco markers for stability comparison), and handles low-quality footage, reflections, and noise through adaptive preprocessing and filtering. The system will compute angular deviation, lateral displacement, and max movement frame-by-frame, and output an annotated video, CSV data, and a clean movement visualization. I’ve previously delivered similar tracking and measurement systems in challenging environments, ensuring reliability and precision. I can deliver the first working prototype within 2–3 weeks, including source code and clear documentation, and I’m confident this can scale into a long-term collaboration. Thanks

@sekai1116

As a seasoned full-stack and AI expert, I bring over 8 years of diverse experience in web and mobile app development, with a particular focus on artificial intelligence. I have significant expertise in computer vision and image processing, making me the ideal candidate for this project. My proficiency with OpenCV and familiarity with object tracking techniques (including optical flow, feature tracking, and blob detection) perfectly match the needs you've outlined. My previous work in low-quality video handling will prove valuable when it comes to taming reflections from stretch film and working with less-than-ideal video conditions. I propose an approach that starts with comparing the tracking reliability yielded by simple visual points versus ArUco markers to ensure the most accurate output for your specific requirements. Using my strong background in industrial measurement systems, I'll build a robust solution that calculates angular deviation from vertical, lateral displacement, maximum movement during the test, overlays tracking points on videos along with a reference line, exports data in CSV format, and helps visualize movement over time. Just as importantly, I assure you of my commitment to deliver your project on time and at reasonable cost. Choose me for this project to leverage my depth of skill and vast experience in enhancing computer vision systems for real-world applications.