YOLO CCTV Theft & Customer Analytics

@AwaisChaudhry

Hi, I’ve read your need for a single turnkey app that runs YOLO-based theft detection, grocery product recognition, and live customer counting on both Windows with NVIDIA GPU and Raspberry Pi 4. I’ll build a clean, cross‑platform codebase that accepts RTSP streams or NVR capture cards without reconfiguration, with a YOLO model fine-tuned on common supermarket items to keep false positives low. The system will raise a REST webhook on theft events, show on-screen bounding boxes and confidence for items and people, and export hourly customer counts to CSV/JSON. I’ll provide installers for Windows and Raspberry Pi, plus a simple dashboard for live feeds, counts, and alerts, and clear guidance to add new SKUs later. The project will ship with well-commented code and a README that explains how to retrain the model from scratch and how to integrate optional accelerators like TensorRT or Coral if you choose. What is the preferred webhook endpoint and authentication method for theft alerts? Would you like me to support multiple camera profiles (RTSP groups) with automatic stream health checks? Do you want auto-scaling cache for bounding boxes to reduce render latency on high-resolution streams? Which six-hour test set should I use for evaluating false positives, and how should it be split across cameras? Are you open to using ONNX as the runtime for portability between Windows and Raspberry Pi? Do you want optional GPU acceleration on Raspberry Pi (e.g., Coral TPU) or rely o

@LiveExperts

Hello, As an experienced computer engineer and developer, I’m confident my skills align perfectly with your CCTV theft and customer analytics project. Not only am I well-versed in Python, which is crucial for efficient YOLO implementation, but I also have extensive knowledge and hands-on experience working with Computer Vision, Linux, and Machine Learning. These proficiencies will ensure the development of a single, turnkey application that meets your specific needs. Additionally, my team and I are proficient in Windows as well as Raspberry Pi 4 programming. This dual expertise is pivotal for ensuring your application can perform equally on Windows and Raspberry hardware. Moreover, having worked on projects involving various types of video sources, including IP streaming via RTSP and analog units through capture cards, we can create a program that can adapt to different sources seamlessly. Finally, our commitment to producing clean, well-commented code with comprehensive READMEs ensures you'll be able to understand and easily re-train your YOLO model from scratch if desired. With Live Experts® LLC by your side, you can rest assured that smooth inference speeds on 1080p streams and low false-positive rates will be met – not to mention delivering within deadlines while keeping constant communication. Let's discuss how we can bring this project to life! Thanks!

@Mahihassi

With my background in Electrical Engineering and an MSc in Embedded Systems, I'm uniquely positioned to tackle a project as intricate as yours. As your CCTV theft and customer analytics application will require an impeccable hardware-software synergy, my expertise in Microcontrollers and Firmware Development becomes invaluable. I've worked extensively with high-resolution videos and complex hardware setups that involved adaptable video feeds, bringing bespoke solutions to different data streams - all valuable experience for managing your diverse camera sources without the need for manual reconfiguration. Building upon my strengths, I've also cultivated considerable knowledge in Computer Vision and Machine Learning domains, through complex projects including Object Detection and Recognition from live video feeds. This equipped me to exactly recreate your requirement, leveraging the powerful YOLO model, which I'll fine-tune using data emphasizing supermarket items to minimize false positives even under crowded shelf scenarios. Additionally, my deep understanding of Python-based frameworks such as PyTorch/ONNX enables me to ensure compatibility across both Windows (desktop with NVIDIA GPU) and Raspberry Pi 4 platforms.

@ZohaibRoy

⭐⭐⭐⭐⭐ Build a Real-Time Shoplifting Detection System with YOLO ❇️ Hi My Friend, I hope you're doing well. I've reviewed your project requirements and see you are looking for a turnkey application to monitor your CCTV feeds and detect shoplifters. Look no further; Zohaib is here to help you! My team has successfully completed 50+ similar projects in computer vision and real-time detection. I will create a robust system that runs on both Windows and Raspberry Pi while ensuring efficient product recognition. ➡️ Why Me? I can easily develop your shoplifting detection system as I have 5 years of experience in computer vision, machine learning, and real-time applications. My expertise includes Python programming, OpenCV, and YOLO for object detection. I also have a strong grip on deploying applications across different platforms, ensuring smooth functionality. ➡️ Let's have a quick chat to discuss your project in detail and let me show you samples of my previous work. Looking forward to discussing this with you in chat. ➡️ Skills & Experience: ✅ Python Programming ✅ YOLO Object Detection ✅ OpenCV ✅ Real-Time Video Processing ✅ Machine Learning ✅ Raspberry Pi Development ✅ REST API Integration ✅ Data Export (CSV/JSON) ✅ User Interface Design ✅ Video Stream Handling ✅ Performance Optimization ✅ Code Documentation 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

@efanntyo

Hi there, I’m excited to propose a turnkey, cross‑platform CCTV analytics solution tailored to YOLO-powered theft detection, live customer counting, and on-shelf product recognition for grocery environments. From your requirements, you need a single code-base that runs on Windows with NVIDIA GPUs and on Raspberry Pi 4, accepts both RTSP streams and NVR/capture-card feeds without manual reconfiguration, and delivers near real‑time performance with low false positives. I’ve designed and delivered end-to-end CV/ML systems for retail analytics, emphasizing robustness, maintainability, and ease of re-training so you can extend SKU coverage over time. What I’ll deliver - A turnkey Python architecture built around the latest YOLO (and optionally ONNX/TensorRT paths) for real-time detection of shoplifters, groceries, and customer heads, with per-object bounding boxes and confidence scores displayed on the live feed. The system is designed to fall back gracefully to CPU-only inference on Raspberry Pi, with optional CUDA/accelerated backends when available on Windows. - A unified video ingest layer that supports both RTSP streams and NVR/capture-card inputs, auto-detecting stream types and resolving authentication and network issues with robust retry logic. No manual reconfiguration required for new cameras. - Real-time theft detection logic that triggers an event or REST webhook the moment a suspicious removal is detected. You’ll get a configurable alert pipeline, with a sample web

@hireageek

Hi there, I’m excited about your project on developing a turnkey application for real-time CCTV theft detection and customer analytics. With my extensive experience in computer vision and machine learning, especially with YOLO, I am confident in creating a solution that meets your specifications. I have completed numerous projects that involve real-time video processing and analytics, earning consistent 5-star reviews as a top freelancer in California. The system I propose will effectively distinguish grocery items and trigger alerts for suspected thefts, ensuring a low false positive rate. I will implement the solution to seamlessly handle both RTSP streams and older analog video sources. Additionally, I will ensure the application runs efficiently on both Windows (utilizing NVIDIA GPU) and Raspberry Pi 4. To facilitate future updates and training on new grocery SKUs, I’ll provide clear instructions. I’d love to discuss your needs further and clarify any specific details. What specific groceries would you like to prioritize for detection and counting? Best regards,

@ASirbescu

Hello, I can definitively meet your expectations and deliver the turnkey solution you're looking for. As a full-stack developer with a specialization in Python, I have had extensive experience working on projects involving machine learning, therefore, I am very comfortable dealing with the core model of YOLO, fine-tuning it, and working with Python, OpenCV, PyTorch/ONNX for your specific project. Moreover, my background in JavaScript will ensure that I create an interactive and intuitive dashboard for you to monitor the live feeds and access customer analytics easily. In terms of streaming RTSP over IP and accessing the NVR through capture cards variation issue you mentioned, my expertise will guarantee the application's compatibility with both video sources without manual re-configuration. This flexibility is crucial for your needs. Lastly, one strength that sets me apart is being results-driven. With this project, I won't be satisfied until you are. So when it comes to ensuring smooth 25-30 fps inference on Windows with GPU and ≥10 fps on a Raspberry Pi using CPU or a USB accelerator as well as maintaining a low false-alarm rate of ≤5% on theft detection, you can rely on me to optimize everything to match these numbers or better them. Together we'll build a powerful system that not only meets your current needs but is also adaptable for future adjustments like adding new grocery SKUs. Let's get started! Thanks!

@softsolution2000

I am confident that my skills in C Programming, Python, Linux, Machine Learning (ML), and C++ Programming are a great match for the "YOLO CCTV Theft & Customer Analytics" project. I am eager to meet the requirements for real-time theft detection, customer analytics, and product recognition using YOLO model. I assure you that the budget can be adjusted after discussing the full project scope, and I am committed to delivering within your budget. Please review my 15-year-old profile for relevant experience. I am ready to start working on this project and showcase my dedication. Looking forward to discussing the details with you.

@Muhammadzeesha59

With over 6 years of experience as a Full Stack Developer, I bring forth a massive skillset that perfectly fits your needs. Having successfully delivered numerous projects within time and budget constraints, I'm confident in my ability to meet your specific requirements for the YOLO CCTV Theft & Customer Analytics application. My extensive expertise in C Programming, C++ Programming, Linux, Machine Learning (ML), and Python, aligns perfectly with your project. Throughout my career, I have developed and optimized applications to perform at their best across different platforms. My deep understanding of object-oriented programming principles gives me an edge in providing you with clean, well-commented code that will enable ease in rerunning and retraining the YOLO model from scratch as mentioned in your Acceptance criteria. Being proficient in Python, OpenCV, PyTorch/ONNX, CUDA will ensure that the application you seek runs smoothly on both Windows (desktop with NVIDIA GPU) and Raspberry Pi 4.

@arzoof1

Hello, I specialize in building real-time AI vision systems, and your project is exactly the kind I love—turnkey, smart, and performance-critical. I can develop a YOLO-based application that spots shoplifters, tracks grocery items, and counts customers live—running seamlessly on both Windows with GPU and Raspberry Pi 4. Your diverse camera inputs, real-time alerts, and low false-positive requirement are fully achievable with a clean, well-documented Python/OpenCV/PyTorch pipeline, optionally accelerated with TensorRT or Coral TPU. I will deliver installers, live dashboards, CSV/JSON exports, and a modular system that makes adding new SKUs effortless. Best Regards, Arzoo Farooq

@markoa7

Hello, I’ve reviewed your YOLO CCTV project and I’m confident I can deliver a turnkey solution that runs on Windows with CUDA GPUs and Raspberry Pi 4, and supports both RTSP streams and capture-card sources without reconfiguration. The pipeline will focus on groceries, with a YOLO-based detector fine-tuned on common supermarket SKUs, plus a real-time people counter and live bounding boxes with confidence scores. I’ll provide a cross-platform codebase, a lightweight dashboard, a REST webhook for theft events, and hourly exports. Documentation will cover retraining the model and adding new SKUs. Are you planning on using a specific RTSP server or NVR for ingestion, or should I build a universal adapter?

@quan9824

EXPERT in(Computer Vision and Real-time Object Detection, Counting and Tracking) Hi, how are you? I checked your detail carefully. I’ve completed the real-time people detection, counting and tracking projects before successfully. Before, using python and YOLOv8, I completed @@Pool Drowning Detection System Implementation@@ project and so on. You can check my works history on my portfolio. I am sure this field and I will do my best. I always thought "It is your job, it is also my job". Awarding me will be the fastest way to complete your task with the best rates possible. THANK YOU.

@SakibKaiser

Hello, I will deliver a turnkey YOLOv8 solution that transforms your CCTV into an autonomous security and analytics hub. Having previously deployed YOLOv8 to detect 5,000+ wind turbines with 92% accuracy, I specialize in high-density object detection where precision is critical to reducing false alarms. The Value I Provide: Hardware Agnostic Performance: I will implement ONNX and TensorRT optimization to ensure you hit 30 FPS on Windows and 10+ FPS on Raspberry Pi, maximizing your hardware ROI without requiring expensive upgrades. Unified Data Stream: I’ll build a robust input handler that auto-detects RTSP and capture cards, providing a "plug-and-play" experience regardless of your camera setup. Actionable Insights: Beyond detection, I’ll integrate REST webhooks for theft and automated CSV exports for foot traffic, turning raw video into business intelligence. Long-Term Autonomy: You receive a complete retraining pipeline and documentation, allowing you to add new grocery SKUs independently and eliminating future vendor lock-in costs. I focus on building a reliable, low-maintenance system that protects your inventory and optimizes your staff's time. Let’s get started. Best regards, Shakib A.

@denisSolution

Hello client, I'm Denis Redzepovic, an experienced developer with expertise in Linux, Arduino, C Programming, Computer Vision, Python, YOLO, C++ Programming and Machine Learning (ML). I have worked extensively on diverse Python projects, ranging from backend development and automation to data processing and API integrations. My deep understanding of Python’s libraries and frameworks allows me to build efficient, scalable, and maintainable solutions. I pay close attention to code quality and performance to ensure your project runs flawlessly. With my solid experience, I’m confident I can deliver results that exceed your expectations. I focus on writing clean, maintainable, and scalable code because I know the difference between 99% and 100%. If you hire me, I’ll do my best until you’re completely satisfied with the result. Let’s discuss your project details so I can tailor the perfect Python solution for you. Thanks, Denis

@Feriver

Hello, I understand that you are seeking an experienced developer to deliver a turnkey YOLO-based CCTV solution for real-time theft detection, product recognition, and customer analytics. My approach will include building a unified code-base compatible with Windows (GPU-accelerated) and Raspberry Pi 4, integrating RTSP and analog camera feeds seamlessly, and ensuring smooth 25-30 fps inference on 1080p streams with optimized performance. I will implement on-screen bounding boxes, confidence scoring, live customer counting with CSV/JSON export, and a dashboard for theft alerts. The YOLO model will be fine-tuned on common grocery items, with clear instructions for adding new SKUs, and the full pipeline will include clean, well-documented code with installers and dependency management. Optional accelerators such as TensorRT or Coral TPU will be considered to maximize performance where applicable. I have extensive experience in Python, C/C++, OpenCV, PyTorch/ONNX, and embedded systems, which aligns perfectly with your requirements. My goal is to deliver a reliable, low false-alarm system with maintainable code and full retraining capability. Thanks, Asif

@ayesha0124

Hi there! I’m excited about the opportunity to work on your YOLO CCTV Theft & Customer Analytics project! With extensive experience in computer vision and machine learning, I can create a robust application that effectively monitors CCTV feeds, identifies shoplifters, and manages customer analytics seamlessly. My approach focuses on developing a unified codebase that runs smoothly on both Windows with NVIDIA GPU and Raspberry Pi 4, ensuring it handles various video sources without reconfiguration. I’ll prioritize real-time detection with minimal false alarms by training our models on common grocery items. You'll receive a comprehensive solution with real-time theft alerts, live customer counting, detailed analytics, and clear instructions for future SKU additions. I aim to deliver within 30 days, so we can kick off testing and review. Thanks,

@juanjosec63

Hello, I’m excited about the opportunity to contribute to your project. With strong experience in YOLO-based computer vision pipelines and cross-platform deployment, I can build a single, maintainable application that ingests RTSP and capture-card/NVR sources, runs real-time detection for customers and grocery SKUs, and implements event-driven “suspicious removal” logic that triggers on-screen alerts and REST webhooks. I’ll tailor the solution so the same core codebase runs on Windows with NVIDIA GPU acceleration and on Raspberry Pi 4 with optimized inference (ONNX/OpenVINO and optional Coral/TensorRT paths), while delivering a simple live dashboard, hourly exports, and clear retraining instructions for adding new grocery items. You can expect clear communication, fast turnaround, and clean, well-documented code that meets your FPS and false-alarm targets with a practical test-and-tune workflow. Best regards, Juan

@salesapple

Building a single YOLO based system that runs on both Windows with GPU and Raspberry Pi requires careful optimization and hardware aware design. I can develop a unified Python pipeline using YOLO with OpenCV and PyTorch or ONNX, supporting RTSP streams and capture card inputs without manual reconfiguration. The system will include real time theft event triggering via REST webhook, grocery item detection with bounding boxes and confidence scores, and continuous customer counting with hourly CSV or JSON export. I will optimize inference using CUDA on Windows and lightweight model variants or optional accelerators for Raspberry Pi to meet your FPS targets. The dashboard will display live feeds, current count, and recent alerts. Clean documentation will allow retraining and adding new grocery SKUs easily. I am ready to start immediately. Let’s connect on chat and review your hardware setup. Verma