Real-Time CNN Surveillance Object Detection

@nrajuu07

With my extensive background in Deep Learning and Neural Networks, I believe I am the best freelancer for your Real-Time CNN Surveillance Object Detection project. I have a proven track record of designing and optimizing CNNs for similar purposes, ensuring both speed and accuracy. Moreover, my experience with mixed-environment surveillance aligns perfectly with your needs. In addition to my expertise in building powerful models, I have a strong grasp on data analysis, feature engineering, and predictive modeling - all crucial aspects of creating a fully trained model with reproducible training pipelines. My skills extend to developing real-time inference scripts that can work seamlessly on RTSP feeds like you've mentioned. Combining this with my familiarity in Docker, I can create an efficient headless Linux-based package for easy deployment on your chosen hardware. Furthermore, I want to emphasize my ability to communicate complex ideas effectively and provide clear, actionable instructions. I will not only deliver the project as per your requirements but also provide comprehensive setup guides facilitating easy installations on Jetson as well as generic GPU servers. By choosing me, you're not just hiring a neural network expert; you're hiring a committed partner who will ensure hassle-free operation of your surveillance module, no matter the environment or scale.

@chiragardeshna

Hi there, I am a strong fit for this scope because I have deployed low-latency object detection pipelines on edge devices where accuracy and speed both mattered. I have hands-on experience training and optimizing CNN-based detectors for people, vehicles, and animals, including Jetson-class deployments with RTSP ingestion and webhook triggers. I work with PyTorch and YOLO-style models, TensorRT acceleration on Nvidia hardware, Dockerized Linux services, and structured JSON alert outputs. I reduce risk by benchmarking latency early on target hardware, tuning precision-recall to minimize nuisance alerts, and delivering a reproducible training and deployment pipeline. I am available to start immediately and can move quickly toward a working real-time prototype on Jetson Xavier. Regards Chirag

@WiredAI

Hi, This is a strong fit for my experience. I’ve built real-time surveillance and edge-AI pipelines on NVIDIA Jetson (Xavier / Orin) with strict latency targets. Proposed approach YOLOv8 (TensorRT-optimised) for sub-100 ms inference per frame on Jetson Xavier INT8/FP16 calibration to balance speed vs false-alert control in busy scenes RTSP ingestion → real-time detection → instant REST webhook alert + auto-record trigger Robust handling for indoor + outdoor mixed environments Deliverables Fully trained model + reproducible training pipeline Headless Linux real-time inference service (JSON alerts, webhook-ready) Unit test video clips validating detection & trigger timing Dockerised deployment + Jetson & GPU server setup guide I’ve previously tuned CNNs for low-latency edge surveillance, and can demonstrate performance benchmarks on Jetson. Ready to start immediately. Best regards, WiredAI Ventures

@Wizztron

I recently delivered a project with this exact scope, developing a clean, professional, and user-friendly video-surveillance system that detects humans, vehicles, and animals across indoor and outdoor settings. Your need for seamless integration with sub-100 ms inference on Nvidia Jetson Xavier and automated alerts via REST webhook aligns precisely with my experience. My expertise includes building optimized CNN models using YOLOv8 and PyTorch, coupled with containerized deployment on Linux platforms. While I am new to freelancer, I have tons of experience and have done other projects off site. I would love to chat more about your project! Regards, Noor

@shalomk27

I am a Computer Vision developer with extensive experience in Python and Deep Learning. I can deliver a high-performance surveillance module tailored for the Nvidia Jetson Xavier

@sivasubramanyam9

I have previously trained a similar models for identifying people,cars,dogs etc using YOLO and will be able to provide you a accurate light model and all necessary components for it.

@achyuthanayakadn

I am an Information Science Engineer with an 8.87 CGPA, specializing in low-latency AI pipelines. I have a proven track record of optimizing YOLOv5/v8 architectures for real-time mission-critical applications: Surveillance & Infrastructure: Engineered a Green Corridor system using YOLOv5 and Flask to trigger real-time infrastructure overrides via REST APIs from live video feeds. Medical AI: Developed an Oral-D diagnostic pipeline using YOLOv5 and U-Net, achieving 92% accuracy through specialized CUDA acceleration. Automation: Built a VTU Result Analyzer using Python and MongoDB, demonstrating my ability to handle complex data scraping and backend integration. I can deliver a Dockerized module for your Jetson Xavier that ensures <100ms inference, utilizes FFmpeg for automated recording, and pushes JSON alerts via webhooks.

@SdeShahid

Hi, I’m Md Shahid Ansari. I’ll build a Jetson‑Xavier ready CNN that detects people, vehicles and animals in under 100 ms and pushes JSON alerts to your REST webhook while triggering stream recording. I have delivered real‑time vision pipelines before – the Freshness Check System used YOLOv8 and OpenCV on edge devices, and the Farm AI project used LSTM‑based forecasting on limited hardware. I’ll fine‑tune a YOLOv8 (or Faster RCNN if you prefer) on your annotated data, wrap the inference in a Docker container, and expose a lightweight FastAPI endpoint that reads RTSP streams, runs inference, and sends alerts. The training pipeline will be reproducible with Python, PyTorch, and Docker, and I’ll include unit‑test clips that verify detection and webhook payloads. A step‑by‑step Jetson setup guide will cover JetPack, CUDA, and required libraries. I am confident I can meet the latency and accuracy targets and look forward to getting started.

@sanketpatil51515

Hi, I’m Sanket, an AI & Computer Vision developer with hands-on experience building real-time detection systems for surveillance and edge devices. Your requirement is clear: Detect humans, vehicles, and animals in mixed indoor/outdoor feeds, trigger a REST webhook alert, and start recording instantly — all under sub-100ms inference on Jetson Xavier. ? My Technical Approach Model Selection YOLOv8 (optimized) for best speed/accuracy tradeoff Converted to TensorRT for Jetson acceleration Mixed-environment dataset fine-tuning (day/night, crowded scenes) Performance Target Sub-100ms inference per frame (optimized FP16/INT8) Reduced false positives via confidence tuning + NMS optimization ? What I’ll Deliver ✔ Fully trained & fine-tuned model ✔ Reproducible training pipeline (PyTorch + dataset config) ✔ Real-time RTSP inference script (headless Linux compatible) ✔ REST webhook JSON alerts ✔ Recording trigger integration ✔ Docker container for deployment ✔ Setup guide for Jetson & GPU servers ✔ Unit test clips proving detection + trigger behavior ? Deployment Ready Dockerized service Runs on Jetson Xavier (CUDA + TensorRT) Clean API output Scalable to multiple camera streams I’ve worked on low-latency computer vision systems and understand the balance between accuracy and nuisance alert reduction. If needed, I can share details of similar detection pipelines I’ve built. Ready to start immediately.

@hemangi3518

Hi, I can build and deploy a real-time CNN-based surveillance module to detect humans, vehicles, and animals from RTSP camera feeds with sub-100 ms inference per frame on Nvidia Jetson Xavier, while keeping high accuracy to minimize false alerts in busy scenes. Proposed approach: Use YOLOv8 (TensorRT-optimized) or a lightweight custom PyTorch model for low-latency inference Mixed indoor/outdoor training with augmentation to handle lighting, occlusion, and camera angles Real-time RTSP ingestion with JSON alerts via REST webhook Trigger recording on detection (event-based capture) Headless Linux runtime with Docker containerization for easy deployment Deliverables (as requested): Fully trained model + reproducible training pipeline Real-time inference script for RTSP with webhook alerts Unit test clips to validate detection + trigger behavior Step-by-step setup guide for Jetson Xavier and generic GPU servers Performance benchmarks (FPS, latency, precision/recall) I’ve worked on real-time CV pipelines and low-latency model tuning for edge devices, including model pruning, quantization (FP16/INT8), and TensorRT acceleration to hit strict latency targets. Thanks Hemangi Chhaya

@prashantr187

Hello, I am interested in developing your Real-Time CNN Surveillance Object Detection system. I have hands-on experience in computer vision and deep learning using Python, OpenCV, TensorFlow, and PyTorch. I can implement a high-performance object detection model such as YOLO or SSD to process live video streams with low latency and high accuracy. The system will include real-time frame processing, bounding box visualization, and optional alert mechanisms. I will also optimize the model for faster inference on CPU or GPU environments. Clean, well-documented code and deployment guidance will be provided. I am ready to start immediately and ensure timely delivery. Best regards, Prashant

@SanjayKrishnanAI

I done lot of projects related to this work and i know how CNN really works because I focusing my career to data science that is having ML, DL, etc..

@chinmoydn

Hi there, I am a Computer Vision expert specializing in high-precision, ground-validated detection. I have achieved global-use-case benchmarks, including 98.6% mAP in detection and 92.1% mAP in vehicle classification. For your Xavier-based surveillance system, I will deliver: Performance: <100ms inference using TensorRT-optimized YOLOv8/v10. Pipeline: RTSP stream ingestion, REST webhook alerts, and automated recording triggers. Deployment: A headless, Dockerized Linux package with a reproducible training pipeline. My focus is on enterprise-grade reliability, ensuring high accuracy to eliminate nuisance alerts in busy environments. Quick question: What is the target resolution of your RTSP streams? Best regards, Chinmoy