Indian ANPR & Face Detection

@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

@hr157

Hi, I'm a computer vision expert with 7 years of experience, specializing in real-time analytics and edge deployments. I've worked with OpenCV, TensorRT, YOLO detectors, and EasyOCR on Indian road footage. My team has deployed ANPR systems with ≥95% accuracy and face detection with high IoU. I'd love to integrate robust plate recognition and face capture into your pipeline, handling night-time, dust, and skewed angles. Demo clips and test results available upon request. Let's discuss.

@rakam26

Hello, Just read your post and it seems you are looking for someone skilled in real-time computer vision analytics, specifically Automatic Number Plate Recognition for Indian licence formats and driver-side face detection on live IP camera streams. With my years of extensive experience and exceptional expertise in OpenCV, YOLO-based detection, OCR pipelines (EasyOCR / PaddleOCR), TensorRT optimization, and low-latency video analytics on H.264 streams, I am 100% confident that I can bring your vision to life in the shortest possible time. I have worked on robust CV systems handling challenging conditions such as night-time IR, skewed angles, dust, and industrial environments, while meeting strict accuracy and latency requirements. Let’s connect and see how great value I can add to your business. When you conect me, I will share my past relevant demos immediatley. Raka

@HASEEB1803

I can help you embed real-time ANPR and driver-side face detection directly into your existing H.264 video pipeline with a strong focus on accuracy, latency, and robustness for Indian industrial environments. My approach ANPR tuned for Indian licence plate formats using YOLO-based plate localisation + OCR (EasyOCR / PaddleOCR), with confidence scoring Robust handling of IR/night footage, dust, motion blur, and skewed angles Driver-side face detection and high-quality frame cropping, timestamp-aligned with the recognised plate Optimised inference using OpenCV + TensorRT to meet sub-300 ms latency at 1080p30 Modular design so both analytics plug cleanly into your current VMS pipeline Why I’m a good fit Hands-on experience with YOLO-based detectors, OpenCV pipelines, and OCR on real-world traffic footage Prior work on vehicle, number plate, and face detection systems under challenging conditions Strong focus on optimisation (batching, frame-skipping, GPU acceleration) to hit real-time SLAs Clean, production-ready Python/C++ code with clear APIs for downstream systems

@ahmadraza001912

With over X years of experience as a full-stack software developer, my core expertise in backend APIs aligns perfectly with the requirements for your Indian ANPR and Face Detection project. I specialize in Python (Django, FastAPI), RESTful & Web APIs, and Database design which are all essential for building robust and efficient analytics systems. I have extensive hands-on experience with OpenCV, TensorRT, YOLO-based detectors, EasyOCR, PaddleOCR, and similar technologies that are relevant to this project. My prior deployments on Indian road footage also include night-time infrared conditions and skewed angles, making me well-versed in overcoming these challenges reliably. My work process focuses on a clear understanding of requirements before coding which includes producing high-quality results even under tight timeframes. My solutions typically have clear hand-off notes for seamless integration and ease of understanding for successors if needed. Given the depth of skill-set I bring matched with the expertise this project seeks in challenging environments, I'm confident in delivering a top-notch solution exceeding all the acceptance criteria for your indented analytics module integration. Languages

@huonghy89

Hi, I hope you are doing well. Very happy to bid your project because my skills are fitted in your project. I’ve deployed real-time video analytics (YOLO/OpenCV) with TensorRT acceleration and OCR pipelines (PaddleOCR/EasyOCR) for ANPR-style tasks, including handling IR/night scenes, motion blur, and oblique/skewed plates in industrial CCTV feeds. I will integrate a low-latency ANPR module into your H.264 ingestion pipeline: plate detection + rectification, Indian-format aware OCR, and per-frame confidence scoring with watch-list triggers, optimized with TensorRT to stay within ≤300 ms at 1080p30. In parallel, I will add driver-side face detection that selects the best-quality crop (sharpness/pose/occlusion scoring) and returns a timestamp-aligned face frame linked to the plate event, plus an evaluation script to prove ≥95% plate accuracy and ≥0.8 IoU on your labeled test set. If you send the message, we can discuss the project more. Thanks.

@ayushk0303

I am well suited for this project because I build production-grade, low-latency video analytics pipelines on top of live H.264 IP camera streams. I have hands-on experience deploying YOLO-based plate and face detectors optimized with TensorRT, achieving sub-300 ms end-to-end latency at 1080p30. I have worked with Indian licence plate formats, handling skewed angles, IR night footage, dust, and motion blur using OpenCV pre-processing and OCR engines like PaddleOCR/EasyOCR with custom post-processing and confidence scoring. I design tightly integrated pipelines that deliver ≥95% ANPR accuracy and high-IoU face crops, ready for real-world industrial security environments.

@baskarrajendran

Hello, I can build a simple and private task manager with strong focus on email reminders, so you never miss a task. The app will work well on both desktop and mobile and stay clean without unnecessary features. You will be able to add tasks, set due dates, receive reminder emails (same day, one day before, or custom), and mark tasks as completed. Email delivery will be handled using Gmail API or SMTP for reliability. Login will be secured with SSL and basic authentication. I can implement this either with a quick no-code setup (Airtable/Notion + Make/Zapier) or as a lightweight custom app using React and Node, based on what is easier for you to maintain. I’m happy to choose the best option after knowing your hosting preference. I will deliver a fully working app hosted on your domain or cloud, reminder system tested with real emails, and a short guide explaining setup and how to change reminder timings. I’m interested to know how you plan to use this daily and what reminder timings you prefer.

@targaryen007

Hello, This is a strong fit for my background in real-time computer vision and video analytics pipelines, especially for production environments with strict latency and accuracy requirements. Understanding of the Task You already ingest live H.264 IP camera streams and want to embed two real-time modules: ANPR for Indian license plates (cars, SUVs, trucks, lorries) with fast plate localization, OCR, and confidence scoring, robust to night IR, dust, and skewed angles. Driver-side face detection to accurately crop and return the best face frame, timestamp-aligned with the detected plate (no identity matching required). All of this must run in-pipeline, with: ≥95% plate read accuracy Face box IoU ≥0.8 End-to-end latency ≤300 ms at 1080p30 My approach would include: YOLO-based plate and face detection fine-tuned for Indian road conditions OCR using EasyOCR / PaddleOCR, optimized and post-processed for Indian plate formats Robust preprocessing for IR, low light, dust, and angle distortion TensorRT optimization for fast inference and stable sub-300 ms latency Clean metadata output aligned with your existing VMS pipeline I prioritize production-ready accuracy, speed, and robustness, not just demo-level results. I can also share sample outputs and demo clips from similar CV pipelines. I’m confident I can meet your acceptance criteria and integrate smoothly with your existing system.

@laxmanm21

Indian ANPR + driver face detection for VMS using YOLO/TensorRT, 95%+ accuracy, real-time <300ms latency at 1080p

@priyanshu0832

I have worked with numerous object detection projects , also i have a paper written on automatic number plate detction , i have experience in collecting the dataset , building the model improving the accuracy , also i have worked with ip camera streams.