AI Motion-Alert CCTV Software

@vladang

This sounds less like a CCTV viewer and more like a distributed detection system, where each hub needs to process streams locally and only send useful alerts back to the main PC. I’d start by building the hub service around RTSP/ONVIF ingestion, frame sampling, reconnect handling, and GPU/CPU-aware model inference, then expose a simple config file for camera URLs, thresholds, alert rules, and hub limits. For detection, I’d likely use a lightweight YOLO-style model with class filtering for humans, vehicles, and wildlife, then tune confidence thresholds separately for daylight and IR/night footage to keep false positives down. The main PC would receive JSON/REST alerts with timestamp, class, confidence, snapshot path, and hub/camera ID, while the dashboard stays lean: live feed, boxes, and alert history. One useful addition would be per-camera sensitivity profiles, since indoor, driveway, and wildlife-facing cameras behave very differently. Ready to start once sample streams are available.

@mahig47

Hello! As per your project post, you are looking to build a stand alone AI powered CCTV monitoring platform designed for large scale surveillance environments. The system will process RTSP and ONVIF camera streams across multiple analytics hubs, detect humans, vehicles, and wildlife in real time, and instantly deliver alert snapshots to a centralized monitoring workstation. My focus will be on delivering a scalable hub based surveillance solution featuring AI powered object detection, RTSP and ONVIF camera integrations, distributed video analytics, real time alert generation with image snapshots, centralized monitoring, configurable alert rules, live camera views with bounding boxes, alert history management, hub health monitoring, and support for approximately 40 cameras per hub depending on hardware specifications. I specialize in AI video analytics, computer vision, OpenCV, TensorFlow, YOLO based detection systems, distributed architectures, real time monitoring platforms, and enterprise software development. My focus will be on building an efficient and maintainable solution that delivers fast detection, accurate classification, and seamless operation across hundreds of camera streams. Let’s connect to discuss the deployment architecture, detection requirements, and performance expectations so we can build a reliable large scale AI surveillance platform tailored to your environment. Best regards, Nikita Gupta.

@vishal1145

Your 768-camera deployment will collapse if you try running YOLO inference on every frame from 40 streams per hub without GPU batching and frame-skipping logic. Most integrators miss this and end up with 8-second latencies that make real-time alerts useless. Before architecting the hub system, I need clarity on two constraints: What GPU specs are you planning per hub? A single RTX 3060 can handle 40 streams at 5 FPS inference, but integrated graphics will choke at 10 cameras. What is your acceptable alert delivery time under network congestion? If a hub loses connection to the main PC for 30 seconds, do you need local buffering or can alerts drop? Here is the architectural approach: - YOLOV8 + OPENCV: Deploy YOLOv8n (nano) model optimized for edge inference with TensorRT acceleration, achieving sub-500ms detection on 1080p frames while maintaining 92% mAP for human/vehicle/animal classes. - PYTHON + FASTAPI: Build a hub service that batches frames from 40 RTSP streams, runs inference every 3rd frame to stay under 2-second latency, and exposes REST endpoints for alert push with base64-encoded snapshots. - DISTRIBUTED ARCHITECTURE: Implement MQTT broker on main PC to handle 768 camera alerts with QoS 1 guarantee, preventing message loss during network hiccups and supporting horizontal scaling to 100+ hubs. - FRAME SKIP + MOTION PRE-FILTER: Use OpenCV background subtraction to trigger YOLO inference only when motion exceeds threshold, reducing GPU load by 70% and cutting false positives from static objects like swaying trees. - DOCKER + NVIDIA-RUNTIME: Package hub software as containerized service with CUDA support, allowing you to deploy across Ubuntu/Windows hosts without dependency conflicts. I've built similar multi-camera AI systems for 2 security firms processing 200+ streams per site. The failure mode is always GPU memory exhaustion when devs try processing every frame - let's discuss your hardware budget and network topology before I finalize the inference pipeline.

@PINESCRIPTERR

This is bigger than a single “camera app”, so I’d treat it as a hub-based detection system from the start. For the first version, I’d build one hub service that reads RTSP/ONVIF streams from a config file, runs motion filtering plus object detection for humans, vehicles, and animals, then sends alert snapshots and JSON data back to the main PC. Once that hub is stable, we can repeat the same setup across the rest of the hubs. With 768 cameras, that architecture matters more than a fancy dashboard. I’d likely use Docker on Linux for the hub side if your hardware allows it, with OpenCV plus a fast object detection model such as YOLO. The dashboard can stay lean: camera status, recent alerts, snapshots, bounding boxes, and a log. I’d also include reconnect handling, confidence thresholds per class, cooldown rules, and camera grouping so false alerts don’t flood the operator. The 40 cameras per hub target will depend heavily on GPU/CPU specs, FPS, resolution, and whether we analyze every frame or sample intelligently. I’d benchmark with your sample streams before promising final hub sizing. A few things I’d want to clarify: 1. What hardware do you expect each hub PC to have, especially GPU if any? 2. Are most cameras outdoor, indoor, or a mix? 3. Should alerts go only to the main PC, or also to phones/email later? Regards, Houssame

@godesignpk

Hello, We went through your project description and it seems like our team is a great fit for this job. We are an expert team which have many years of experience on JavaScript, Python, Software Architecture, Machine Learning (ML), C++ Programming, OpenCV, Computer Vision, AI Model Development Lets connect in chat so that We discuss further. Regards

@flapjakc

Hey! Smart CCTV motion detection in Windhoek — 768 cameras across hub-based architecture, real-time human/vehicle/wildlife detection with push alerts and snapshot delivery to a main PC. Well-defined technical spec. Python with YOLOv8 for detection — fast, accurate, low false-positive rate. Hub-based design: each hub handles up to 40 RTSP/ONVIF camera streams, runs local inference, sends alert snapshots and JSON payload to the main PC. Under 2-second end-to-end latency on 1080p. Human, vehicle and wildlife detection with bounding boxes. Configurable alert thresholds, RTSP URL changes via config file only — no code changes needed. Docker image for easy deployment on Windows or Linux hubs. REST endpoint pushing timestamp, object class, confidence and snapshot path. Test metrics included.

@juanpablomacf

I’m Juan Pablo, computer‑vision engineer, and I can deliver a hub‑based CCTV analytics system that runs real‑time human / vehicle / wildlife detection across your 768 RTSP/ONVIF cameras with <2‑second alert latency. I’ve built similar multi‑node pipelines using Python/C++ + OpenCV + TensorRT/ONNX, with per‑hub GPU/CPU optimization and push‑alert services. My approach: each hub ingests ~40 cameras, performs motion gating + object detection, and sends snapshot alerts (JSON + image) to the main PC. The dashboard stays lean—live feed, bounding boxes, alert log, and config. The model will be tuned for low false positives in indoor/outdoor + IR conditions, using lightweight detectors optimized for 1080p streams. You’ll get a compiled app or Docker image, full source, REST endpoint for alerts, reconnection logic for RTSP changes, and a small test set with accuracy metrics. The system will run on Windows or Linux—whichever lets us ship fastest without licensing issues.

@FizzaNadeemK

Hi, you need a distributed CCTV intelligence system that can process hundreds of camera feeds reliably and alert you only when meaningful events happen, not another basic monitoring dashboard. The hub-based design for 768 cameras is the critical detail because analytics placement, stream handling, and alert delivery will determine latency and false positives, and I’ve worked with automation and software integrations where real-time data processing and reliable workflows were essential. I’ll design the system around RTSP/ONVIF ingestion, hub-level AI detection for humans, vehicles, and wildlife, real-time snapshots, and a central dashboard with alert logging and configurable endpoints. I’ll structure the deployment with configurable camera sources, documented setup, and testing against sample streams to validate performance before scaling. How much GPU capability is available on the planned hub PCs, since that will define the detection model and camera capacity per hub? Best regards, Fizza Nadeem K

@Specgi

Hello, I am excited to submit my proposal for your AI Motion-Alert CCTV Software project. With experience in computer vision, AI-powered video analytics, and surveillance system development, I can build an intelligent solution that accurately detects motion events, minimizes false alarms, and provides real-time notifications. My expertise includes working with OpenCV, deep learning models, object detection algorithms, and video stream processing for security applications. For this project, I can develop a robust system capable of analyzing CCTV feeds, distinguishing between relevant and irrelevant motion, and triggering customizable alerts through email, SMS, mobile notifications, or dashboard interfaces. I can also implement features such as person and vehicle detection, event recording, cloud integration, and performance optimization to ensure reliable operation across different camera setups and environments. I am committed to delivering a scalable, efficient, and user-friendly solution tailored to your specific security requirements. I would be happy to discuss your existing CCTV infrastructure, desired AI capabilities, and deployment preferences to create a system that provides accurate monitoring and actionable alerts. I look forward to contributing to the success of your project.

@mugencodes

Hello, I understand your need for a stand-alone CCTV monitoring application with a focus on smart motion detection and instant alerting. With 768 cameras to integrate into the system, I propose a hub-based approach where hubs conduct analytics and send push notification photos to the main PC. Each hub can accommodate up to 40 cameras based on its specifications. For this project, I will leverage my expertise in Python, AI Model Development, Machine Learning, C++ Programming, JavaScript, Software Architecture, OpenCV, and Computer Vision. I will use OpenCV, TensorFlow, or a comparable framework to develop a reliable algorithm that accurately identifies humans, vehicles, and wildlife in real-time, minimizing false positives in varying lighting conditions. The deliverables will include a compiled application or Docker image for your server, source code with installation steps, a JSON or REST endpoint for alert data, and a test video set showcasing detection accuracy. I will ensure an end-to-end latency of under 2 seconds and maintain a low false-positive rate as per your requirements. I look forward to fine-tuning the system with your sample streams once we finalize the approach. Best regards, Jayabrata Bhaduri

@mobasherreza10

With significant experience in the intersection of AI/ML, software development and DevOps, I am the perfect candidate for your AI Motion-Alert CCTV Software project. Having a thorough understanding of critical frameworks such as OpenCV and TensorFlow aligns perfectly with your needs. My outstanding skills in JavaScript and Python combined with extensive work on Machine Learning preprocessing and training models will ensure not only high accuracy but low false-positive rates, even in complex lighting conditions. I am committed to meeting your performance expectations. I will create a platform that provides an incredibly fast end-to-end latency of under 2 seconds from motion to alert on a 1080p stream, while maintaining an impressively low false-positive rate - below 5% in daylight and below 10% at night with IR activation. Another crucial aspect is ensuring seamless camera RTSP URL reconnectivity without code changes. Count on me to build it smoothly, providing you with an intuitive dashboard that shows the current feed, bounding boxes, and an alert log.

@markupdudes

Hi, I have carefully reviewed your requirements for an AI Motion-Alert CCTV Software focused on real-time detection of humans, vehicles, and wildlife with low latency and high accuracy. Leveraging proven frameworks like TensorFlow and OpenCV, I will develop a hub-based system capable of analyzing up to 40 cameras per hub and delivering push notifications with snapshots to your main PC efficiently. My approach ensures the system meets strict false-positive thresholds and handles seamless RTSP stream reconnections. I will provide the compiled app, source code, API endpoint, and detailed accuracy metrics as requested. Let's discuss sample streams to optimize detection accuracy What are the hardware specifications of the hub PCs intended for processing 40 cameras each? Best regards,

@Lincolnwhiz

Building on my track record of delivering high-performing web applications, I am confident in my ability to design and develop the advanced AI-Motion Alert CCTV software you need. With proficiency in JavaScript, Machine Learning, and Python - which are key languages for developing AI models - I am well-versed in creating intelligent systems that can reliably recognize, differentiate, and alert based on various objects in real-time. I have extensive experience working with platforms such as OpenCV and TensorFlow, both of which I believe will optimize your project's desired outcome. With me on board, you'll get more than just coding prowess but a thorough understanding of your needs and dedication to a long-term solution that saves you time and money. Let's come together to build something that is not just smart but fast, scalable, and ready for growth!

@cuyodigital

I looked at your CCTV monitoring requirements and need an AI motion detection system with real-time object classification and instant alerting. The system must handle 768 cameras across hubs with 40 cameras per hub, with under 2-second latency and less than 5% false positives in daylight. For object detection, I recommend YOLOv8 for its proven performance in human, vehicle, and wildlife classification. The system will use OpenCV for video processing and TensorFlow for model inference. Each hub processes streams locally and sends alert snapshots to the central monitoring station, reducing bandwidth usage and improving response time. I am confident I can deliver a robust solution because I have built similar AI video analytics systems for large-scale surveillance deployments, including a 500-camera smart city project with real-time object detection and alerting. The software stack will be Python with OpenCV, YOLOv8, and TensorFlow, packaged as a Docker container for cross-platform deployment. The system will support RTSP and ONVIF streams, provide a lean web dashboard, and expose a REST API for alert data. The price is 900 USD and the timeline is 10 business days. Do you have a preferred hub PC specification for load testing? Should the system support custom alert rules for specific camera groups? Let me know your answers. I can start right away. Ricardo

@himanshus642

With over 5 years of hands-on experience as a Full Stack Developer, I am confident that I possess the skills and expertise required for this AI-based CCTV monitoring project. My proficiency with technologies such as Python, OpenCV and Machine Learning, will enable me to create a reliable, efficient algorithm for accurate object detection while maintaining a low false-positive rate. Additionally, being well-versed in software architecture and SaaS platforms, I understand the importance of creating a robust yet user-friendly system. Having successfully built multiple web-based and standalone applications serving different industries, including FinTech and automation, I understand the criticality of delivering projects on-time without any compromise on quality. My meticulously-built applications have not only been scalable and performant but also meet stringent security requirements due to the nature of the industries they've served. In conclusion, with my technical expertise in AI model development, Software Architecture and Full Stack Development skills complemented by my focus on clear communication and milestone-first approach I believe can deliver you a top-notch motion-alert CCTV software. Together let’s build a functional application that exceeds your expectations. Looking forward to discussing your project further!

@rafaels170

Hello, your architecture requirement makes sense and the key challenge is not viewing 768 cameras but performing reliable real-time AI analytics with low false positives and sub-2-second alerts. I can build a hub-based system using RTSP/ONVIF streams, OpenCV and modern object detection models to classify humans, vehicles, and wildlife, with centralized alert management, snapshot notifications, REST/JSON endpoints, auto-reconnect, and Docker deployment. Before confirming the final estimate, could we review the expected hub hardware specifications and sample streams together? I’m confident we can build a scalable platform that meets your latency and accuracy targets. Looking forward to working with you.

@chiragpipal

⚠️ If you're not happy, you don’t pay. ⚠️ Hi, Thank you for checking my proposal and sharing the detailed project brief. I can build your stand-alone CCTV monitoring application focusing on smart motion detection using OpenCV or TensorFlow, ensuring high accuracy and minimal false positives. I will deliver: • Advanced human, vehicle, and wildlife detection • Hub-based system handling 40 cameras per hub • Lean desktop/web dashboard with real-time feed and alerts • Compile application/Docker image ready for your server • Source code with detailed README • JSON/REST endpoint for alert data • Test video set with detection accuracy metrics You will also receive documentation for easy setup and training. I am confident I can execute your vision professionally. Looking forward to discussing the next steps. Best regards, Chirag.

@sakshigweb

Hi — A hub-based system at 40 streams per hub needs the detection pipeline designed right — YOLOv8 inference across 40 concurrent 1080p RTSP streams under 2 seconds requires GPU acceleration per hub. CPU-only won't hit your latency target reliably. Architecture: Python hub service with RTSP/ONVIF ingestion via OpenCV, YOLOv8 with CUDA acceleration, configurable confidence thresholds keeping false positives under 5% daylight / 10% night IR. Each hub processes its streams and pushes alert data (timestamp, class, confidence, snapshot) via REST/JSON to the central PC. Config-driven — swap RTSP URLs, system reconnects without code changes. Docker image for clean hub deployment. Central PC receives push notifications with snapshot images, bounding box overlays, and a web-based alert log dashboard. Deliverables: Docker image per hub, source code with README, REST endpoint spec, and detection accuracy metrics across all three classes on your test streams. Hardware note: each hub PC needs an NVIDIA GPU for the 40-stream / 2-second target — happy to recommend minimum specs once hub hardware is confirmed. What GPU hardware do the hub PCs currently have, or is hardware selection still open? Regards Sakshi G