Multi-Camera Kitchen AI System Development

@ZohaibRoy

⭐⭐⭐⭐⭐ Create a Custom Multi-Camera Kitchen Automation System ❇️ Hi My Friend, I hope you're doing well. I've reviewed your project requirements and see you're looking for a Computer Vision Engineer. You don't need to look any further; Zohaib is here to help you! My team has successfully completed over 50 similar projects focused on kitchen automation systems. I will set up the multi-camera streaming, develop the AI model, and ensure a smooth cloud upload process, all while providing a user-friendly dashboard. ➡️ Why Me? I can easily build your custom kitchen automation system as I have 5 years of experience in computer vision and deep learning. My expertise includes Python, OpenCV, and working with YOLO models. Additionally, I have a strong grip on cloud architecture and video processing technologies, ensuring a comprehensive solution for your project. ➡️ Let's have a quick chat to discuss your project in detail. I can also share samples of my previous work that demonstrate my capabilities. Looking forward to chatting with you! ➡️ Skills & Experience: ✅ Computer Vision ✅ Deep Learning ✅ Python ✅ OpenCV ✅ YOLOv8/YOLOv10 ✅ Cloud Architecture ✅ RTSP Streaming ✅ Video Processing ✅ AWS S3 ✅ NVIDIA GPU Handling ✅ Video Encoding ✅ Frontend Development Waiting for your response! Best Regards, Zohaib

@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

@devendrathakur12

Hello, I can develop your multi-camera kitchen automation system using Python, OpenCV, YOLO, PyTorch, and FFmpeg. I have experience building real-time computer vision systems, object detection, video processing, and cloud integrations. The solution will process all camera streams, detect bowl movement and pouring actions, capture the required 3-second clips, and upload them automatically to cloud storage. I will also build a simple web dashboard for viewing clips by station and time. The code will be clean, scalable, and optimized for GPU processing with clear documentation and deployment instructions. Best regards, Dev S.

@corpmember29

Hello, I have carefully reviewed your requirements and understand that you need a multi-camera AI-powered kitchen monitoring system capable of detecting ingredient additions using standardized prep bowls, capturing precise 3-second video clips around the pouring action, and synchronizing those clips to a cloud-based dashboard for quality control. I have 10+ years of experience in AI, computer vision, video analytics, cloud integrations, and real-time processing systems. I have expertise in Python, OpenCV, YOLO-based object detection, RTSP video streaming, GPU-accelerated processing, cloud storage solutions, and web-based monitoring dashboards. I can develop a complete solution including multi-camera RTSP ingestion, real-time bowl detection and tilt-event recognition, rolling video buffer management, FFmpeg-based clip generation, cloud upload automation, and a lightweight dashboard for reviewing clips by station, date, and time. The architecture will be optimized for edge processing on NVIDIA GPU hardware, ensuring reliable performance across all 8 camera streams while minimizing storage and bandwidth requirements through efficient video compression and event-driven uploads. I would be excited to discuss your hardware setup, training data availability, cloud preferences, and implementation timeline in more detail. Thank you. Christina

@quan9824

&& YOLO, OCR, OpenCV, Tensorflow, PyTorch, Keras, ML/DL model && Hi, How are you?. I have full skills and full experiences of this field. I have developed many Image Processing project and I am expert in these fields I can finish your project with high quality and on time. Please send me your message to discuss more about your project. I am waiting your reply now. Thanks.

@PINESCRIPTERR

This is a strong use case for edge AI, especially because you’re standardizing the bowls. That makes the detection problem much more realistic than trying to infer every ingredient by itself. I’d build the first version around one or two stations first: RTSP ingest, rolling buffer, bowl detection, tilt/pour trigger, 3-second clip extraction, compression, upload, and dashboard playback. Once the trigger is reliable, scaling to all 8 stations is mostly a pipeline/performance problem. For the model, I’d use YOLO for the bowl detection and add a lightweight action layer based on position, angle, motion, and cooking-zone overlap. If needed, we can fine-tune on your kitchen footage so the system learns the exact bowls, lighting, camera angle, and station layout. The edge server can run all streams locally, then only upload the short clips to S3/GCS/Azure to keep bandwidth and storage costs under control. The dashboard can stay simple: station, date/time, clip preview, filters, and download/open actions. A few things I’d confirm early: 1. Are the prep bowls already chosen, or can we help choose colors/shapes that are easiest to detect? 2. Do you have sample overhead footage from the actual kitchen? 3. Which cloud platform do you prefer for storage and dashboard access? Regards, Houssame

@ricardo686

Hi there, I’ve worked on real-time computer vision pipelines where multiple video streams are processed simultaneously for event detection, so the core challenge here—multi-camera sync, low-latency inference, and precise event-based clipping—is very familiar. Main risk in systems like this is timing accuracy between detection and video buffer extraction. With YOLO-based detection on 8 RTSP streams, even small latency drift can cause clips to miss the actual “action window” unless the buffering and inference timestamps are tightly synchronized at the edge layer. I’d build this as an edge-first pipeline: RTSP ingestion → GPU-based YOLO inference per stream → timestamped event triggers → rolling circular buffer (FFmpeg/OpenCV) that captures exactly 3 seconds around the detected tilt action → compressed clip export and async upload to S3/Cloud Storage. A lightweight dashboard (React or Streamlit) would then index clips by station and time for quick review. Have experience building video processing systems involving OpenCV, real-time inference pipelines, and edge GPU optimization where latency and frame accuracy were critical. I can deliver a stable multi-camera system with accurate event-based clipping, optimized GPU usage, and a simple cloud dashboard for reviewing all generated clips in near real time.

@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, Linux , Windows , Cloud , Azure . 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

@Aaleen110

I understand you need a computer vision system to automatically detect when new ingredients are added to cooking pots or pans using 8 cameras in a commercial kitchen, triggering a 3-second video clip. My experience developing real-time object detection models for similar automated workflows, including a project that successfully identified and logged specific actions in a high-throughput environment with 95% accuracy, directly aligns with your goal. I will build a custom solution using YOLOv8 for ingredient bowl detection and tracking across your 8 camera feeds. This will involve developing a Python application leveraging OpenCV for video stream processing and PyTorch for model inference. The system will output timestamped detection events and trigger cloud storage uploads (e.g., to AWS S3) for the 3-second video clips. What is the desired format and metadata for the captured 3-second video clips? Ready to start as soon as you confirm scope.

@usmanbashir12100

Hello, I’m interested in this project and have experience with computer vision, real-time video analytics, YOLO-based object detection, edge AI deployments, cloud integrations, and dashboard development. ### How I would approach it: * Deploy a multi-camera RTSP ingestion pipeline capable of processing all 8 PoE streams concurrently on the edge GPU server. * Train and optimize a YOLOv8/YOLOv10 model to detect your standardized prep bowls and identify tilt/pour events with high accuracy. * Implement a rolling video buffer that captures 1 second before and 2 seconds after the detected action. * Compress and upload clips automatically to AWS S3, Azure Blob Storage, or Google Cloud Storage. * Build a lightweight dashboard (React, Streamlit, or Retool) for filtering and reviewing clips by station, timestamp, and event type. * Add monitoring, logging, and confidence scoring to reduce false positives. ### Relevant Experience: * YOLOv8/YOLOv10, OpenCV, Python * NVIDIA GPU optimization (TensorRT, CUDA) * RTSP video processing pipelines * Edge AI deployments * AWS, Azure, Google Cloud * React and Streamlit dashboards * Event-driven video analytics systems The proposed architecture is well suited for edge inference, minimizing bandwidth usage while maintaining near real-time performance across all cooking stations. Best regards, **Muhammad Usman**

@alizain721

You want a hands free system that grabs a precise 3 second clip when a color coded prep bowl tilts over a cooking zone across eight overhead PoE domes. That tilt timing plus low false positives is the hard part, not just detecting a bowl. Most teams miss that short arm motions and steam can look like pours. Reliable detection needs both per object tracking and a small temporal action classifier or flow check to avoid spurious triggers. I built a multi camera QA system for a food packaging line using YOLOv5 for object detection, a temporal classifier for actions, FFmpeg rolling buffers and S3 uploads. How I would deliver it * Ingest eight RTSP streams to a central process with per stream circular buffers and synchronized timestamps * Train a lightweight YOLO model on your bowl colors and add a compact temporal module or optical flow check to confirm tilt * On trigger extract 1 second before and 2 seconds after, transcode with FFmpeg for size, upload to your S3 bucket with station and timestamp metadata * Provide a simple React or Streamlit dashboard for Station based playback and management Do you have sample footage and the exact bowl color codes available? Which cloud do you prefer and what retention or bandwidth limits should I assume so I can prepare an architecture diagram and timeline.

@CorporateRD

Good day! Thanks for sharing the detailed project brief. If you already have any diagrams, camera setup layout, sample footage from your kitchen stations, or any reference system in mind, please share it as well so we can align everything properly before starting the build. From what I understand, you are looking to build a real-time multi-camera AI system for a commercial kitchen that automatically detects ingredient pouring actions using overhead RTSP cameras. The system should identify specific color-coded prep bowls, detect the tilt/pour event, and then extract a precise 3-second video clip around that action. Right? Questions: What type of visual conditions should we expect inside the kitchen during operation, including lighting changes, steam, heat, and camera angle variations, so we can decide how robust the detection model needs to be for real-world performance? How do you want the system to behave if multiple cooking stations trigger events at the same time, and should the processing prioritize real-time upload or batch processing to avoid any delay in video delivery? I would love to schedule a meeting or have a detailed conversation about the project to clarify some concerns about the project, this way I can demonstrate my capabilities perfectly and suggest the best result possible. Regards! AN from Royal Designs Budget and timeline are placeholders

@demivision

Hi there, Thank you for sharing the detailed requirements for your Multi-Camera Kitchen AI System. We’re Demivision LLC, a team with deep expertise in computer vision and real-time video processing, and we’re excited by the opportunity to help automate quality control in your commercial kitchen. We fully understand your vision: capturing precise, hands-free video documentation of ingredient additions using standardized prep containers, all orchestrated by a robust, AI-driven, edge-to-cloud workflow. Our team has extensive experience building multi-camera RTSP streaming pipelines and deploying custom YOLO-based models for object detection and action recognition—skills perfectly matched to your need for reliable detection of prep bowl movements and the critical “tilting/pouring” gesture. Our proposed approach is to establish a high-performance RTSP ingestion pipeline on your central GPU workstation, ensuring parallel, real-time processing for all 8 stations. We will train or fine-tune a lightweight YOLOv8/YOLOv10 model tailored to your color-coded containers, integrating custom action segmentation logic to trigger the rolling buffer and extract the required 3-second video clips. For video handling, we’ll utilize FFmpeg for efficient, lossless encoding and compression, minimizing storage costs and upload times. On the cloud side, we’ll implement a secure, scalable upload pipeline to your preferred platform (AWS, Google Cloud, or Azure) and deliver a streamlined, mobile-friendly dashboard using React or Streamlit for easy clip review by management. We’ve successfully delivered similar end-to-end vision systems in industrial settings (please see portfolio links in our profile), and we’re confident we can help you achieve a seamless, scalable solution for your kitchen operations. Looking forward to discussing your project in more detail! Best regards, Demivision LLC

@sufyanasmat38

As professional software and AI developers with 9+ years of combined experience, my team and I have all the skills needed to deliver on your custom kitchen AI system project. Our deep expertise in Computer Vision, Deep Learning, and Web Development means that we can adeptly set up the multi-camera RTSP streaming pipeline, develop and train an object detection/action segmentation model using YOLO workflow, build the Edge-to-Cloud pipeline, develop frontend interfaces for cloud video playback, and more. We are not only well-versed but highly proficient in the necessary programming languages (Python, Java, C++, JavaScript), cloud platforms (AWS S3/Lambda, Google Cloud), edge processing hardware (Jetson/GPU workstations), and video processing tools like FFmpeg. This broad-based knowledge makes us best positioned to minimize costs while delivering quality results. Moreover, our commitments don't end once the project is over; we value long-term collaborations and are open to ongoing retainer/maintenance contracts. The possibilities extend beyond this project’s description. Let's get started on maximizing your commercial kitchen's efficiency today!

@mughiraaa

Hello, Your project is very interesting and aligns well with my experience in computer vision, real time video processing, AI based detection systems, and cloud connected applications. I have worked on projects involving object detection, action recognition, video event capture, GPU accelerated processing, and cloud storage workflows. The approach of using standardized color coded prep bowls is practical and creates an excellent foundation for achieving high detection accuracy across all cooking stations. I am confident in developing the complete workflow from multi camera stream processing and event detection through clip extraction, cloud upload, and dashboard access. I would love to schedule a quick discussion to review your hardware setup, accuracy expectations, and deployment timeline. I am confident we can deliver a reliable solution that operates efficiently across all 8 stations. I will share my portfolio in chat I look forward to hear from you. Thanks Best Regards, Mughira

@dataspro

Nice to meet you , My name is Anthony Muñoz, I express my interest in working on your project after carefully reading the requirements and concluding that they match my area of knowledge and skills. I am currently the lead engineer for the IT agency DSPro and I have more than 10 years of experience in the field. I have successfully completed a large number of similar jobs and I consider your project to be a challenge in which I would like to work and be able to make it a reality. Please feel free to contact me, it will be my pleasure to help you. I greatly appreciate the time provided and I remain attentive to any questions or concerns. Greetings

@markupdudes

Hello, I am excited about your Multi-Camera Kitchen AI System project and confident in delivering a precise, efficient solution. With extensive experience in Azure cloud services and video processing, I will develop a YOLO-based object detection model tailored to track your color-coded prep bowls and capture exact 3-second clips around the tilting action. Leveraging my skills, I will ensure robust RTSP stream handling from your eight cameras, optimize real-time processing on your NVIDIA GPU, and enable seamless cloud uploads with a user-friendly dashboard. I propose starting with a detailed review of your current setup and a prototype model within two weeks, aiming for a fully operational system shortly after. How do you envision the user experience on the mobile dashboard for viewing and managing video clips? Thanks,

@mobasherreza10

With a comprehensive background in computer vision, deep learning, and video processing, I have the knowledge and skills needed to bring your Multi-Camera Kitchen AI System to life. My experience includes developing and training object detection models using YOLO as desired for this project. Additionally, my proficiency in edge processing hardware such as NVIDIA's Jetson combined with my cloud architecture understanding (AWS S3, Lambdas or equivalent) makes me an ideal fit for this Edge-to-Cloud pipeline job. I've successfully handled costs associated with video encoding and compression using FFmpeg to reduce cloud storage expenses while maintaining high-quality results. Moreover, my solid grasp of real-time multi-camera RTSP video pipelines is highlighted by my previous projects utilizing similar infrastructures in conjunction with Python, OpenCV, PyTorch/TensorFlow.

@zahidhasta

Hi, This project aligns very well with my background in AI/ML and computer vision. I have experience building real-time video analytics systems using Python, OpenCV, PyTorch, YOLO, FFmpeg, RTSP pipelines, edge AI devices, and cloud integrations. For your kitchen automation system, I would implement a scalable multi-camera architecture capable of processing all 8 overhead PoE streams simultaneously on a central GPU workstation. The pipeline would detect and track the standardized prep bowls, identify the tilt/pour action, maintain a rolling video buffer, automatically extract the required 3-second clip (1 second before and 2 seconds after the event), compress it efficiently, and upload it to cloud storage with station and timestamp metadata. I particularly like the fact that bowl colors and shapes are standardized, as this significantly improves detection reliability while keeping inference lightweight and real-time. My approach would combine YOLO-based detection with event logic and tracking to minimize false triggers and ensure only valid ingredient additions generate clips. I can also build the cloud upload workflow and a clean dashboard for reviewing clips by station, date, and time, while keeping the system easy to maintain and extend. I have worked on multi-camera analytics, industrial monitoring, object detection, tracking, and edge AI deployments, and can provide a robust production-oriented solution rather than just a proof of concept. Best regards, Zahid Hassan