Computer Vision Developer for RTSP Video Processing & Real-Time Detection

@TheElitePro

Hi, I have done two projects to implement detection on real time feed. Both the projects were done on Nvidia Jetson. I can implement the phase 1, just let me know if you want this to be done on a local PC or an embedded board. I can provide you timeline and budget once we decide this. Let's connect. Regards, Vishal

@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.

@amirrmusavifr

Hi, I’m an AI expert with professional experience in computer vision, with a proven track record of working on complex image processing and AI/ML model development. With skill sets: • Algorithm Development: Strong understanding of computer vision algorithms and techniques, including convolutional neural networks (CNNs), object detection, image segmentation and feature extraction. • Model Training & fine-tuning: Develop and train machine learning models tailored for image analysis and visual data interpretation. I have worked on some well-known models like YOLO, RCNN, U-Net, Deeplab, ViT etc. • AI Integration: Implement and integrate AI models into existing software and hardware systems, ensuring high performance and scalability. • Data Analysis: Analyze and process large datasets of images and video feeds to identify patterns, trends, and insights. • Data Handling: Experience in handling and processing large datasets, including image and video data. Familiarity with data augmentation techniques and synthetic data generation. • Performance Optimization: Optimize algorithms and models for real-time processing and ensure they can handle large-scale data efficiently. • Programming Skills: Proficient in programming languages such as Python. Experience with deep learning frameworks like TensorFlow, PyTorch, or Keras. • Tools & Libraries: Proficiency with OpenCV, scikit-image, and other relevant libraries. Experience with version control systems like Git.

@mkh559a5a1fd66e9

With extensive experience in both computer vision and AI development, I am thrilled to tackle this RTSP video processing project. Utilizing my deep understanding of Python and OpenCV, I can seamlessly connect to the RTSP stream, process the live video feed, and implement human detection using a lightweight model such as YOLO. Having achieved near real-time performance working on similar projects before, delivering 8-10 FPS shouldn't be an issue for me. My ability to handle RTSP streams effectively combined with my familiarity with popular object detection models like YOLO, TensorFlow and PyTorch makes me the ideal candidate for your project. Moreover, my broad knowledge of video pipelines such as FFmpeg/GStreamer would be a significant asset in executing this work effortlessly. Rest assured, my solution will run smoothly on your local machine without any cloud dependencies. You will gain more than just a working demo, source code and setup instructions from me; you'll experience first-hand my dedication to clean, maintainable code and impeccable deployment processes through CI/CD pipelines. As someone who continuously seeks innovation in technology implementations, I'm excited for the potential that our collaboration holds for revolutionizing your real-time video processing concept. Let's get started, together!

@devanshkhandekar

Hi,I'm a seasoned Applied ML Engineer(6+ yoe) with hands-on experience in real-time video analytics,RTSP/IP camera pipelines,lightweight detection models,tracking & OCR-based live video systems. Relevant projects: >>Indian ANPR pipeline: built a real-time video system using RTSP/video input,vehicle + plate detection,OCR & frame-wise processing with practical speed/accuracy tradeoffs >>Realtime tracking + OCR workflows: worked on pipelines where objects were detected in live video,tracked across frames & passed to OCR/analytics modules for stable downstream results >>IP camera / stream-based CV systems: handled OpenCV-based live stream ingestion,buffering,frame skipping & near-real-time inference on local machines >>Video analytics prototypes: built detection pipelines with bounding-box rendering,stream display & modular code ready for later phases like tracking,event logic,or alerts My approach for Phase 1 would be: >>connect robustly to the RTSP stream >>optimize frame read/inference/display pipeline for stable 8–10+ FPS >>use a lightweight detector (YOLO-family or equivalent) for human detection >>draw clean bounding boxes & ensure the system runs locally without cloud dependency >>keep the code modular so future phases can add tracking,zone logic,counting,re-ID,OCR,or alerts without rewriting the base pipeline I’d typically use Python + OpenCV,with optional FFmpeg/GStreamer support if stream stability needs tuning.

@satyaevolfast

Hello, I’m excited about the opportunity to develop a real-time video processing prototype focused on human detection from RTSP streams. With my strong background in Python and experience in OpenCV, I am well-equipped to handle your requirements. For this project, I propose to implement a video processing pipeline that connects seamlessly to the RTSP stream, utilizes a lightweight model like YOLO for object detection, and displays bounding boxes in real time. I plan to ensure a performance of at least 8–10 FPS by optimizing the data flow and processing efficiency. My recent project, Pixeal, involved developing an AI-driven annotation platform, which shares similarities in object detection and real-time processing. I can deliver a working demo, source code, and setup instructions within 5 to 10 days for Phase 1, and my team at ASPL is ready to support scaling if needed. Relevant work demonstrating my experience with IP cameras and real-time video analytics can be shared upon request. Let's discuss the final details to ensure alignment. Best regards, Satya

@manoval09

Hi, Real-time RTSP processing with reliable frame detection is challenging—stream latency, frame drops, and inference speed bottlenecks cause most projects to fail. I've built several multi-stream vision pipelines that consistently achieve 30+ fps throughput with sub-100ms detection latency across concurrent feeds. I'd use OpenCV for RTSP ingestion with frame buffering to prevent drops, run detections via YOLOv8 with TensorRT quantization on GPU for consistent inference speed, and implement async processing so the detection loop never blocks the stream reader. This architecture keeps your pipeline accurate and your streams flowing without interruption. First step: I'll review your stream specs—resolution, frame rate, detection classes—and deliver a working proof-of-concept showing single-stream detection at your target fps within 24 hours. What's your required detection accuracy, and are you targeting GPU or CPU-only deployment? Best regards, Val

@rishus081

Are you looking for reliable, scalable, and high-quality software development? You’re in the right place. I specialize in building custom software solutions tailored to your business needs — whether it's a web application, backend system, or full-stack development. What I offer: Custom software development Web application development (frontend + backend) REST API development & integration Database design and optimization Bug fixing and performance improvements System automation tools Technologies I work with: Frontend: React, HTML, CSS, JavaScript Backend: Node.js, Python, Java, PHP Databases: MySQL, PostgreSQL, MongoDB Others: Git, Docker, cloud services Why choose me: Clean, maintainable code On-time delivery Clear communication Scalable and secure solutions

@abdurrehman373

Real-time RTSP stream processing requires a high-performance pipeline that balances detection latency with system resource management. I can deliver a robust solution focused on stability and frame consistency, ensuring you hit your 8–10 FPS target without system bottlenecks. My approach for this prototype: * Integration: Use OpenCV with threaded RTSP ingestion to prevent frame drops. * Detection: Implementation of YOLOv8-tiny or similar lightweight models optimized for CPU/CUDA local inference. * Optimization: Utilizing Numba or optimized NumPy operations to minimize latency between inference and render. * Delivery: Clean, modular Python codebase with documented setup instructions for local deployment. While I am establishing my presence on this platform, I have extensive experience building Python-based automation and computer vision pipelines. I prioritize code efficiency and maintainability, ensuring that this Phase 1 prototype is scalable for your future requirements. I am ready to begin immediately and can provide the demo within your 10-day timeline. Are there specific camera resolutions or hardware constraints I should optimize for during development?

@MohitKumar777x

Hi, It's within my wheelhouse. I have done projects with Python, OpenCV, real-time video processing pipelines, such as from an RTSP stream, along with human detection algorithms using YOLO. I'll design a minimalistic Phase 1 demo that: * Connects with the RTSP stream (from IP camera/NVR) * Handles video feed from live stream * Uses human detection algorithm (lightweight version of YOLO) * Shows bounding boxes * Has ~8–10 FPS locally I'll make it straightforward and efficient, and document it for easy running. Timeline: 5-7 days Outcome: Minimalistic demo program with code and documentation In addition, I'll make sure the project architecture facilitates future phases seamlessly. Looking forward to discussing more.

@AsifTech21

Do you already have an RTSP stream from your IP camera/NVR available for testing? I can build a robust real-time video processing prototype by setting up a stable RTSP ingestion pipeline using OpenCV integrated with FFmpeg for efficient frame decoding, then deploying a lightweight YOLO-based detection model (optimized via TensorRT or ONNX Runtime if needed) to ensure consistent 8–10+ FPS performance on a local machine. My approach focuses on minimizing latency through frame skipping, batching strategies, and hardware acceleration where available, while maintaining high detection accuracy for human subjects. I will deliver a clean, modular Python codebase with clear setup instructions, along with a working demo showcasing live bounding box detection from your IP camera/NVR stream. The impact you can expect is a reliable, scalable foundation for future phases—ready to extend into advanced analytics like tracking, alerts, or behavior recognition without reworking the core pipeline.

@khrystynad4

Hello, I can build the Phase 1 real-time video processing prototype for RTSP human detection. This project fits my background in Python, OpenCV, computer vision, real-time processing, debugging, and performance-focused system design. I understand the goal is a working local demo: connect to an IP camera/NVR RTSP stream, process frames live, detect humans using a lightweight YOLO-style model, display bounding boxes, and keep performance near real time. My approach would be: 1. Set up RTSP ingestion using OpenCV, FFmpeg, or GStreamer depending on stream stability. 2. Implement human detection using YOLOv8/YOLOv5-nano or another lightweight model. 3. Filter detections to the person class and draw bounding boxes/confidence scores. 4. Optimize FPS using frame skipping, resized inference, threaded capture, and GPU support if available. 5. Add basic logging and error recovery for dropped RTSP frames. 6. Provide clean Python source code, requirements file, and setup/run instructions. 7. Deliver a working demo video or live screen recording. Timeline: 5–10 days for Phase 1. Fixed price: $150–$300 for Phase 1, depending on hardware target and RTSP stream complexity. I can also structure the code so later phases can add alerts, tracking, zones, recording, or dashboard features.

@shokoufeh1994

I've built real-time RTSP video pipelines using OpenCV and Python, handling multi-stream ingestion, frame buffering, and object detection with YOLO or custom models. I know the latency tradeoffs between processing every frame vs. frame-skipping strategies. For your project I'd set up an RTSP reader with OpenCV's VideoCapture, run detection in a separate thread pool to avoid blocking, and push results to a queue for downstream processing. I'd also handle stream reconnection on dropout, which is a common pain point. I can have a working prototype processing a live RTSP feed with detection output in the first few days. What resolution and frame rate are you targeting, and do you have a preferred detection model or should I propose one?

@harshithnm

Hi, I can build a real-time RTSP video processing system with human detection using a lightweight YOLO model. I’ll ensure smooth 8–10 FPS performance with optimized frame handling and clear bounding box visualization. You’ll get clean Python code, real-time detection, and simple setup instructions. I can start immediately and deliver a working prototype within 2 days. Estimated effort: 10–12 hours. Rate: $12/hour. Let me know if you have any specific camera or hardware requirements. Thanks

@vishnurm2001

Hi, I can build your RTSP-based real-time human detection system with stable performance and clean output. I have experience working on real-time video analytics systems including PPE detection, surveillance, and multi-camera pipelines using YOLO and RF-DETR. For your project, I will: * Connect and process RTSP stream(s) reliably * Perform human detection using an optimized YOLO model * Display/output video with bounding boxes in real time (8–10+ FPS) * Ensure smooth and efficient processing on your local system If a GPU is available, I can implement this using NVIDIA DeepStream with TensorRT optimization for higher performance, lower latency, and easier scaling to multiple cameras. Otherwise, I can deliver a well-optimized OpenCV-based pipeline that meets your requirements. I can complete Phase 1 within 5–7 days. I am willing to share a video demo before you commit. Best regards, Vishnu

@SaefuL4m1n

Hello, i have Computer Vision Developer for RTSP Video Processing & Real-Time an experienced Computer Vision / AI developer to build a real-time video processing prototype. Support function: - Connect to RTSP stream (IP camera / NVR) - Process live video feed - Implement human detection - Display bounding boxes on detected objects - Ensure near real-time performance (minimum 8–10 FPS) And i have skills about : - Strong experience with Python and OpenCV - Experience handling RTSP streams - Familiar with object detection models (YOLO, TensorFlow, PyTorch, etc.) - Experience with video pipelines (FFmpeg / GStreamer is a plus) - Able to run solution on local machine (no cloud dependency) - Working demo, yes

@alejandrof81

"I am the ideal candidate because I specialize in high-performance systems. With projects like Moonshadow-NVR, I’ve proven I can build lightweight, C++/FFmpeg-based solutions that handle heavy video processing without draining CPU resources. I deliver stable, scalable code tailored to your specific hardware."

@omara0056

​Hi, I can develop this real-time human detection prototype for you using Python and OpenCV. ​I have extensive experience with SSD MobileNet, which is a very lightweight and fast model, perfect for high FPS and real-time RTSP streaming on standard hardware. I’ve already built similar systems and can handle the stream optimization to ensure minimum latency (No lag). ​I am ready to test your RTSP link and provide a working demo immediately. Looking forward to working with you!

@yugansh1gupta

I am a Computer Vision and AI developer with hands-on experience in building real-time video processing applications using RTSP streams. I have previously worked on a project involving live camera feeds where I implemented real-time processing and analysis. This included handling RTSP streams, processing video frames efficiently, and integrating AI-based detection systems. For your requirement, I can: Connect and stream video from RTSP sources (IP camera / NVR) Implement real-time human detection using lightweight models like YOLO (You Only Look Once) Optimize the pipeline to achieve smooth performance (8–10 FPS or higher) Display bounding boxes and detection results in real time Build a clean, modular Python-based solution using OpenCV and deep learning frameworks like PyTorch / TensorFlow I also have experience working with video pipelines and can integrate tools like FFmpeg if needed for stream handling and optimization. What you can expect: Fully working real-time demo Clean, well-documented source code Easy setup instructions for local deployment Delivery within 5–7 days for Phase 1 I focus on building efficient, practical solutions that are ready for real-world deployment and scalable for future phases.