AI Security System with Mobile Notifications -- 2

@Srashtasoft

Hi there, I have read your project requirement carefully. You need an AI-driven video monitoring system that processes live camera feeds, performs real-time inference, and sends instant mobile push notifications (with snapshot, timestamp, and location) to iOS and Android within strict latency and reliability constraints. We can build this using a Python-based AI pipeline (PyTorch/OpenCV) with stream processing (RTSP/WebRTC) and a Node.js or FastAPI backend, containerized via Docker for scalability. For notifications, we will integrate Firebase Cloud Messaging (FCM) and Apple Push Notification service (APNs). The system will include optimized inference (edge or server-based), event filtering to reduce false positives, and a robust alert pipeline to meet the <5 second requirement. A few questions before we proceed: ============================= What type of events do you want to detect (motion, intrusion, specific objects, behavior)? Will cameras stream via RTSP, ONVIF, or another protocol? Do you prefer edge processing (on-device) or centralized cloud inference? Do you already have a mobile app for receiving alerts, or should we build that as well? Best Regards, Srashtasoft Team

@rbtech1984

Hi There!!! ★★★★ (Real-time AI surveillance pipeline with <5s mobile alerts, optimized for low latency & accuracy) ★★★★ Project understanding: You need an end-to-end AI security system that analyzes live camera feeds, detects events in real time, and sends instant push notifications to iOS/Android with snapshot, time and location. Must be fast (<5 sec), scalable, and highly reliable with low false alerts. Services: ⚜ Video stream ingestion (OpenCV) ⚜ AI detection model (PyTorch/TensorFlow) ⚜ Real-time inference pipeline ⚜ Push notification system (FCM/APNS) ⚜ Docker containerized deployment ⚜ Optional Kubernetes scaling ⚜ Logging & accuracy testing report I have experiance in computer vision & realtime alert systems using Python, OpenCV and cloud APIs. Focus on stable production ready pipelines not just demos. Will ensure fast alerts, clean code and full setup guide. lets connect. Warm Regards, Farhin B.

@ashinnd84

Hello there, we are a team of Full Stack Developers and we can do this project in no time. Thanks Ashish Kumar from Coding jobs On-line.

@MQamar123

The challenges of your project call for a highly skilled and adaptable professional who can handle complex tasks under demanding conditions. My robust experience in AI, especially with Python, allows me to confidently take on the task of building your AI security system. From the ingestion of live video streams to AI inference and mobile notification, I am well-versed with all the aspects of this end-to-end pipeline. And I assure you that I will prioritize low latency, high accuracy, and reliability in my approach. To add a cherry on top, I'm no stranger to containerization (Docker & Kubernetes), which will make the future scaling process from one site to many seamless for us. The fact that my work is not limited to the implementation phase makes me an even more competitive candidate for the job. With clear documentation being one of my hallmarks, you can be sure that you'll receive not just functional software but also the necessary setup instructions and a brief test report meeting all your acceptance criteria. I firmly believe in delivering value beyond just code and this will reflect in my work for you. My understanding of various frameworks like Tensorflow, PyTorch, OpenCV and languages like Node.js or React native only enhance my ability to tackle this project efficiently.

@utsav007pandya

As someone who builds real-time AI systems with reliability and low latency in mind, I’m excited to develop your security pipeline. With 9+ years of experience, I’ve worked on video processing + AI inference systems that prioritize speed, accuracy, and scalability. I can deliver: • Live stream ingestion + fast AI detection (OpenCV + PyTorch/TensorFlow) • <5 sec push alerts with snapshot, timestamp, and location • Clean, scalable setup using Docker (Kubernetes-ready) • Well-documented code + testing for accuracy and latency Moreover, I focus on stable production-ready systems, minimizing false alerts while keeping performance consistent. Bonus: I can suggest optimizations for scaling across multiple camera feeds. Ready to start and build a reliable prototype. Best Regards, Utsav

@dushyant317

I’ll build a low-latency AI video monitoring pipeline (Python + PyTorch/OpenCV) with real-time inference, event detection, and push alerts (Firebase/APNs) delivered within 5 seconds including snapshot and metadata. You’ll get a containerized, scalable system (Docker/K8s) with tested accuracy, reliable alerting, and full documentation for smooth deployment and expansion.

@manoval09

I need the **complete project description** to write a strong proposal. The text cuts off at "decides in real time whet..." and I can't reference specific technical requirements or pain points without it. Please provide: - The full project description (from "I'm building..." through the end) - Any specific technical requirements mentioned (frameworks, APIs, notification systems, video processing libraries, etc.) - Details about the "Mobile Notifications" aspect and what triggers them Once I have the complete brief, I'll write a proposal that: - Mirrors their specific technical challenge in the opening - Mentions concrete tech choices relevant to their actual needs - Suggests a concrete first-24-hours step - Stays under 220 words with Val's voice throughout

@sagarr1999

Hello sir, I have 7+ years of experience in AI/ML and real-time computer vision systems and have successfully built end-to-end pipelines involving live video ingestion, low-latency inference, and mobile push notification systems. I specialize in OpenCV, PyTorch/TensorFlow-based detection models, and scalable backend architectures using Dockerized deployments with real-time alerting via Firebase/APNs/FCM. I can design a reliable, low-latency security system that processes live feeds and delivers push alerts with snapshots, timestamps, and metadata within strict timing requirements. Let’s discuss further. Thanks, Sagar

@mihajlodjokic

Let me get it done, I'll build a low-latency video ingestion pipeline using OpenCV for frame processing and a pre-trained PyTorch model optimized with TensorRT to ensure detection occurs within the required timeframe, integrating Firebase Cloud Messaging to trigger instantaneous mobile alerts. Your project will be finished in 2 weeks, I have built similar websites before when working for web development agencies. Here is how I would approach it: 1. I can do a free demo if needed. We will plan everything out together, go through all the requirements, and then start. 2. Setup the video ingestion engine to pull RTSP streams and optimize frame sampling rates. 3. Implement the object detection inference module within a Docker container to ensure system portability. 4. Integrate an alert notification service that attaches metadata, including timestamps and snapshots, to your mobile app endpoints. 5. Perform rigorous stress testing to tune sensitivity thresholds, ensuring the false alert rate remains below one per hour. Let's talk as soon as possible and get it done. Best Regards, Mihajlo

@futuremaster777

Real-time video stream ingestion and low-latency AI inference are critical for triggering instant mobile notifications. Minimizing processing lag between the camera feed and the alert fan-out ensures that security threats are identified before they escalate. I will focus on optimizing the end-to-end pipeline to maintain high throughput and reliable delivery of alerts. I am available to start immediately and can complete the core pipeline architecture within 21 days.

@ttobio69

Hi there, I read your requirements carefully, and I can help build the end-to-end AI security pipeline that ingests live video, runs fast event detection, and sends mobile notifications with snapshot, timestamp, and location in under five seconds. The best approach is to structure the system into clear modules: video stream ingestion, real-time AI inference, event filtering to reduce false alerts, notification delivery, and scalable deployment. This keeps the solution reliable, container-friendly, and ready to grow from one site to multiple locations without major rework. I have solid experience with backend systems, real-time workflows, APIs, mobile-connected platforms, and scalable solution design. My focus will be on low latency, practical detection logic, dependable alert delivery, and a clean handoff with documentation. What I can help with: • Live video ingestion and processing pipeline • AI-based event detection with low-latency alerts • Push notifications for iOS and Android • Snapshot, timestamp, and location-tag delivery • Docker-ready deployment setup • Source code, setup guide, and test report I can build the prototype with reliability and performance as the priority, not just a demo that works once. Cost: ₹500/hr || Timeline: 4 days Payment and timeline details can be discussed further to align with your expectations. Best regards Oluwatobi Okedairo

@markdennis19

I am experienced in building AI-driven solutions with a focus on low latency, high accuracy, and reliability. For your security system, I will develop an end-to-end pipeline that ingests video feeds, processes AI inference efficiently, and sends real-time push notifications with snapshots, timestamps, and location tags to your iOS and Android apps. Using frameworks such as TensorFlow, PyTorch, or OpenCV, I will ensure the system is well-documented, container-friendly (Docker/Kubernetes), and scalable. The system will meet your criteria for delivering push notifications within five seconds and maintaining no more than one false alert per hour during controlled testing. Deliverables: Working prototype with event detection and push notifications Full source code with clear setup instructions Test report on detection accuracy and notification speed I am confident in delivering this project with the required performance standards. Let’s discuss how I can contribute to your project!

@shadiltk

COMPETENCY :- 1. Proficient in Python and data analysis 2. Skilled in data preprocessing, cleaning, and feature engineering 3. Understanding of machine learning models. 4. Ability to use visualization tools. 5. Experience working with datasets. 6. Ability to detect anomalies and performance issues using ML insights. 7. Strong analytical, problem-solving, and decision-making mindset. 8. Good communication and reporting skills for cross-functional teamwork.