Yolo jobs
...for an experienced AI engineer or technical architect who can help: * Define the MVP architecture * Recommend the most suitable AI models and frameworks * Design a modular, scalable system * Estimate development phases, timeline, and cost * Help prepare a technical roadmap suitable for investors and future development Experience with any of the following would be valuable: * Computer Vision (YOLO, RT-DETR, etc.) * OCR (PaddleOCR, EasyOCR) * Speech-to-Text (Whisper) * Text-to-Speech (Piper or similar) * LLMs (Gemma, Llama, Qwen, etc.) * Edge AI and embedded systems * AI system architecture **Current Stage** This project is currently at the MVP planning stage. We are actively seeking funding and strategic partnerships before beginning full-scale development. If you've wo...
...requirements (no ROI%/yield/annualised-return framing, RICS development-appraisal terminology only) — the compliance logic is defined; you own making it structurally unbreakable rather than instruction-dependent. - Mentor and technically direct the two existing engineers on infrastructure and ML-adjacent work. ## Requirements **Computer vision** - Production experience with object detection/segmentation (YOLO, Detectron2, Mask R-CNN, or transformer-based segmentation) applied to structured/technical imagery — floor plans, CAD, architectural drawings, or comparable (satellite/aerial, medical, industrial). - Geometric reasoning on top of CV output: polygon extraction, area computation, constraint-based spatial reasoning. - Experience with OCR on low-quality scanned/PD...
...usage, and model retraining Expected Output Example: { "plateNumber": "123456", "country": "Qatar", "plateType": "Private", "confidence": 0.94, "timestamp": "2026-07-07T10:30:00", "cameraId": "CAM-01", "plateImage": "path/to/", "vehicleImage": "path/to/" } Important Requirements: * Developer must have previous experience in ANPR, OCR, object detection, OpenCV, YOLO, PaddleOCR, EasyOCR, TensorFlow, PyTorch, or similar technologies. * The system should be trainable/improvable using our own Qatar/GCC plate dataset. * Accuracy should be tested in day, night, low-light, angled, and moving vehicle conditions. * The fin...
...two camera feeds, you'll detect and track four players, hold a stable identity for each across a full match, map their movement into real court coordinates, and turn that motion into meaningful output — highlight clips and per-player stats. You own the models and the data they produce; our team builds the player-facing page on top. What you'll build: Player detection + multi-object tracking — YOLO-class detection with ByteTrack/DeepSORT-style tracking, maintaining stable per-player identity across occlusion and crossover Pose estimation — for gesture-based highlight triggers (e.g. a player holding a T-pose) Court mapping — homography from image plane to real court coordinates (OpenCV); auto-calibration from court lines is a plus Movement analy...
... The model must work across DIFFERENT monitor brands and layouts (Philips, GE, Drager, Mindray, Nihon Kohden, SLE, etc.) — not just one fixed brand. It must understand that different labels mean the same vital (e.g. "HR", "PR", "Pulse", "Heart Rate" all mean heart rate). This is a semantic understanding problem, NOT a simple OCR or bounding-box detection task. Please do not propose Roboflow / YOLO / Tesseract-only solutions — they cannot generalize across unseen layouts. WHAT THE MODEL MUST DO - Input: one image of a patient monitor (often in a cluttered real-world hospital scene, with staff/equipment in frame) - Output: clean JSON, e.g. { "hr": 142, "spo2": 98, "rr": 45, "bp_sys": 70...
Hello, I have been working in data annotation for almost 3 years, gaining extensive experience in annotations. This makes me a valuable addition to your team. In addition, I have much experience in image annotation, segmentation, bounding boxes, polygons, key points, 2D and 3D annotations, and even LIDAR annotations. Tools: C... and even LIDAR annotations. Tools: CVAT Roboflow LabelImg Labelbox VGG Doccano Label Studio Annotation Solutions: Bounding Boxes Image annotation Object labeling/tagging Semantic Segmentation Polygons Annotation/masks Polylines Annotation Key Points annotation Sentiment, Text & Topic Analysis Image classification and categorization Object Tracking Data ...
Educational demonstration that showcases how state-of-the-art YOLO models can spot and differentiate bees, wasps, and other insects directly from video streams.
...want to surface. The very first sport you will tackle is Football, but I want the parameters, event types, and metric catalogue structured so that the same codebase can be extended later to basketball, hockey, and any other field-based sport without rewriting core logic. Core objectives • Multi-camera alignment, calibration, and frame syncing • Real-time player and ball detection / tracking (YOLO, DeepSort, OpenPose, or comparable frameworks) • Automated event recognition so the system can compile full highlight reels—the highest visual priority right now—alongside goal-only, defensive-moment, and candid stills without manual editing (FFmpeg, OpenCV for the assembly pipeline) • Per-player stat extraction focused first on Distance Covered, ...
I’m spearheading an educational demonstration that showcases how state-of-the-art YOLO models can spot and differentiate bees, wasps, and other insects directly from video streams. The ambition is two-fold: create a high-performing detector and produce a concise yet rigorous review of current research, ending with ideas that push the field forward. Here’s the landscape you’ll step into: • Source material: raw, unlabeled videos shot in varied lighting and environments. • Label status: none—so the first milestone is to design and execute an efficient annotation workflow (CVAT, Roboflow, Label Studio, or your preferred stack). • Primary tooling: YOLO26/YOLOv5/YOLOv8 on PyTorch, paired with OpenCV for preprocessing and potential real-time demos. ...
Hello, I have been working in data annotation for almost 3 years, gaining extensive experience in annotations. This makes me a valuable addition to your team. In addition, I have much experience in image annotation, segmentation, bounding boxes, polygons, key points, 2D and 3D annotations, and even LIDAR annotations. Tools: C... and even LIDAR annotations. Tools: CVAT Roboflow LabelImg Labelbox VGG Doccano Label Studio Annotation Solutions: Bounding Boxes Image annotation Object labeling/tagging Semantic Segmentation Polygons Annotation/masks Polylines Annotation Key Points annotation Sentiment, Text & Topic Analysis Image classification and categorization Object Tracking Data ...
## Job Title Computer Vision Engineer: Custom Multi-Camera Kitchen Automation System (YOLO + Cloud Sync) ## Job Description## Project Overview We operate a commercial kitchen with 8 cooking stations and are looking to build a custom, hands-free quality control system. The goal is to automatically capture a 3-second video clip every time a cook adds a new ingredient into a cooking pot or pan. To make this highly accurate and lightweight, we are standardizing our prep containers. Cooks will transfer ingredients into uniform, highly visible, color-coded prep bowls. The AI needs to track these specific containers, detect when they hover and tilt over a cooking zone, crop a 3-second video clip (1 second before the tilt, 2 seconds after), and upload it to a cloud dashboard. ## System Arch...
...Application Integration 15. Security Personnel Management ________________________________________ Advanced Features (Preferred) AI Learning Capability Multi-Camera Support Night Vision Optimization Edge AI Processing License Plate Recognition (Optional) GIS & Location Mapping (Optional) ________________________________________ Technical Requirements Preferred Technologies: • Python • OpenCV • YOLO (Latest Version) • TensorFlow / PyTorch • Deep Learning Models • Face Recognition Frameworks • FastAPI / Django / Flask • React.js / Vue.js • Android (Flutter or Native) • MySQL • Docker • Cloud Deployment Support • REST API Development ________________________________________ Deliverables 1. Complete Source Code 2. ...
My current YOLO 26 model struggles with Eurobox and bread-crate detection, hovering below 50 % accuracy. With only ~100 training images (each holding 30–40 crates), I need to push performance past 94 % without relying on power-hungry cloud instances. I’m open to every practical angle—tighter algorithmic tuning, smart preprocessing and creative data augmentation—so long as the final solution can run locally on a mid-range GPU or even CPU if possible. Feel free to experiment with lighter YOLO variants, pruning, quantisation, mosaic augmentation, rotation/flip tricks, colour tweaks or any other ideas you trust; I care about the end result and the ability to reproduce it on my hardware. Acceptance criteria • Provide the updated model weights, trai...
...together—jersey numbers and face recognition—so that each bounding box you draw really belongs to the right person. I need that tagging to be accurate at least 90 % of the time under normal HD broadcast footage. The program should accept a YouTube URL or live stream, process the frames on-the-fly, and overlay the player’s name or squad number with minimal latency. A GPU-friendly pipeline using OpenCV, YOLO/Detectron, TensorFlow or similar frameworks is perfectly fine as long as it delivers the required accuracy and keeps the frame rate smooth. For clarity, here is what I expect you to hand over: • A Windows executable (or installer) that runs locally without cloud dependence • Source code with clear build instructions • A short user guide s...
...server, mini PC, or cloud processing * Reliable internet connection * Optional GPU device for faster AI processing * POS integration if available ### Software * Camera feed connection * AI video analysis engine * Web dashboard * Alert system * Video clip storage * User login system * Reporting module * Admin settings ## 11. Suggested Technology The development team may use: * Python * OpenCV * YOLO object detection model * Pose estimation model * Deep learning framework such as PyTorch or TensorFlow * Web dashboard using React, , or similar framework * Backend using Node.js, Python FastAPI, or Django * Database such as PostgreSQL or MongoDB * Cloud storage or local encrypted video storage ## 12. Development Phases ### Phase 1: Research and Planning * Study store layout an...
...the images captured and preventive steps can be taken to reduce machine downtime/breakdowns. Scope - Capture images from Camera - Annotate and Classify images - Select and Deploy AI model - Train model - Run the Images through - Provide the feedback from anomalies Activities - Setup image transfer from camera to cloud - Setup the cloud AI solution with Image processor - Setup cloud AI model(s) (YOLO?) - Train model with images - Execute and collect Condition monitoring feedback Out of scope: - image classification and annotation will be done by other team - Robot and HiRes camera is already covered and not needed in this scope. Other: For pilot, first 1-2 weeks on site to make solution run. After pilot: build the Industrial Ready solution, with EDGE AI device, tuned model, ...
I need a small .NET library for real-time object detection using YOLO and ONNX Runtime. The work should be done from scratch. The library will load ONNX weights, run inference on images, and return bounding boxes with class labels and confidence scores. Acceptance criteria -Library detects objects in a sample image with correct boxes and labels -Sample app runs end-to-end and saves an annotated image -README explains setup and basic usage -Code builds without errors on a standard Windowsdev machine
We are developing...AI Accelerator. The system must: Detect another drone using the camera feed Track the detected drone in real time Generate guidance commands to keep the target centered in view Support autonomous follow behavior Preprocess camera images for AI inference Train and optimize object detection models Optimize performance for low latency and high FPS on Raspberry Pi Required Skills: Python OpenCV YOLO/Object Detection Object Tracking (ByteTrack, DeepSORT, etc.) Raspberry Pi Edge AI Deployment Drone Systems and Navigation Preferred Experience: Drone vision projects Autonomous tracking systems Real-time AI inference on embedded devices Raspberry Pi AI accelerator deployment This is a guidance and development support project. Please share similar projects you have ...
I have a single 40-second MP4 clip that shows two motorcycles circulating the same track. Each bike can be separated at a glance because they are painted different colours. What I need is a reliable, frame-accurate measurement of the time interval between the first and the second motorcycle as they pass a chosen reference line on the circuit. Please use YOLO (or an equivalent real-time object detector) to: • detect both bikes throughout the whole sequence, • define a consistent reference line or region on the track, • timestamp the exact moment each bike crosses that reference, and • burn a clear visual overlay onto the video that displays the calculated gap in seconds. The finished deliverable is the processed video with the overlay already embedded; no...
I need a lean, working proof-of-concept that automatically counts foot traffic using a single 360-degree camera. The goal is to drop the unit into busy conference halls, festival entrances, or outdoor promotional zones and have it return reliable head-counts without manual intervention. Here is what matters to me: • Vision logic: Please build or integrate computer-vision models (OpenCV, YOLO, TensorFlow Lite or similar) that detect and track people moving through the camera’s full 360° field of view. The algorithm must distinguish unique passes so that every person is counted once. • Edge or cloud flexibility: I am fine with the model running on a Raspberry Pi 4, Jetson Nano, or a small cloud instance—as long as latency is low and setup remains simple. ...
...from images or video captured by a camera. This project is the first step toward developing a larger industrial inspection platform. ## Scope of Work * Develop a computer vision model for defect detection. * Use YOLO, PyTorch, TensorFlow, or a similar framework. * Train the model using provided sample images. * Create a simple interface/dashboard showing: * Product status (Pass/Fail) * Defect confidence score * Defect image capture * Support live camera feed processing. * Provide source code and documentation. ## Preferred Skills * Computer Vision * Python * OpenCV * YOLO (v8/v11 preferred) * PyTorch or TensorFlow * Real-time video processing * Industrial inspection experience is a plus ## Deliverables 1. Working defect detection prototype. 2. Source code and ...
...and computer vision to build a mobile visual analysis and object-tracking prototype for FPS-style environments. Project Requirements Real-time object/player detection using YOLO (v8 or v10 preferred) Visual overlay system displaying bounding boxes and tracking information Smooth real-time inference with low latency Support for multiple configurable environments via external config files Android overlay UI with stable performance Compatibility with modern Android devices Clean and optimized architecture Optional: Audio event visualization or directional indicator system Required Skills Strong experience deploying YOLO models on Android (NCNN, TFLite, TensorRT, etc.) Android screen capture and overlay rendering Performance optimization for mobile AI inference Experience w...
...Pulp • Canal • Caries • Restoration • Filling • Implant • RCT High-precision contours around Caries, Implant and Restoration areas are the top priority, with equally careful delineation of Pulp and Canal. Please work in the annotation platform of your choice—Labelbox, Supervisely, V7 Darwin, CVAT, or a comparable tool—and export the dataset in COCO JSON (instance segmentation) or YOLO-compatible polygon format. Deliverables 1. A folder of the original X-rays plus their corresponding segmentation files, organised by image ID. 2. A brief QA report that summarises inter-annotator checks or automated validation you employed to guarantee accuracy. All files will be reviewed against clinical ground truth, so consistency ...
I'm seeking a computer vision expert to develop an object detection system specifically for vehicles. This system will be deployed on traffic cameras. Key requirements: - Detect various types of vehicles in real-time - Ens...deployed on traffic cameras. Key requirements: - Detect various types of vehicles in real-time - Ensure high accuracy and reliability - Work in various weather and lighting conditions - Provide a user-friendly interface for monitoring Ideal Skills and Experience: - Proficiency in computer vision frameworks (e.g., OpenCV, TensorFlow) - Experience with real-time object detection algorithms (e.g., YOLO, SSD) - Strong background in machine learning and image processing - Ability to optimize models for edge devices Need to work on site at Gwalior, India...
...NLP pipeline for entity extraction and intent detection. You’re free to choose spaCy, Hugging Face Transformers or a comparable library as long as the final function returns structured JSON with the extracted entities and confidence scores. 2. Object Detection on Images Alongside the text stream, the app receives still-image snapshots that must be analysed for specific objects in real time. A YOLO-v8 or Faster-RCNN model loaded with Torch or TensorFlow is fine, provided inference stays under 150 ms per frame on a mid-range GPU. The detector’s output has to be normalised into the same JSON schema as the NLP results so the downstream service can treat both uniformly. Key expectations • Modular, well-commented Python 3.11 code that drops straight into my FastA...
...detection and OCR recognition Helmet detection for two-wheelers Seatbelt detection Red light violation detection Wrong-side driving detection Over-speed detection Lane violation detection Real-time alerts and logging Store violation data in database Dashboard/Admin panel for monitoring Upload video or use live camera feed Export reports and screenshots of violations Technologies Preferred: Python OpenCV YOLO / TensorFlow / PyTorch OCR for number plates Flask/Django for web dashboard MySQL or MongoDB database Expected Output: The system should detect traffic violations automatically and save: Vehicle image Number plate text Time and date Type of violation Camera/location details Additional Requirements: Clean UI dashboard Proper documentation Source code included Efficient and o...
...detection and OCR recognition Helmet detection for two-wheelers Seatbelt detection Red light violation detection Wrong-side driving detection Over-speed detection Lane violation detection Real-time alerts and logging Store violation data in database Dashboard/Admin panel for monitoring Upload video or use live camera feed Export reports and screenshots of violations Technologies Preferred: Python OpenCV YOLO / TensorFlow / PyTorch OCR for number plates Flask/Django for web dashboard MySQL or MongoDB database Expected Output: The system should detect traffic violations automatically and save: Vehicle image Number plate text Time and date Type of violation Camera/location details Additional Requirements: Clean UI dashboard Proper documentation Source code included Efficient and o...
...project (source code) - APK file - Clear setup instructions: - How to install APK on tablet - How to connect webcam - How to run the app Existing Code (Important) We will provide: - PDF explaining the tracking system (AI + smoothing logic) - Setup guide from previous developer - Existing project files The system includes: - Face tracking (MediaPipe) - Body tracking fallback (YOLO) - Smooth camera motion (PID control) You will need to: - Review and adapt this code for the new webcam input Preferred Tech Stack Developers may use: - Java or Kotlin (Android Studio) - Experience with: - Camera2 API OR USB (UVC) camera integration - MediaPipe / TensorFlow Lite (preferred) - OpenCV (bonus) Important Notes - No backend required - No database required - Foc...
...Upload CT scan or X-ray images AI detects lung nodules Cancer risk prediction Heatmap highlighting suspicious areas Doctor dashboard Patient history tracking PDF medical report generation Technologies Frontend: Vue.js / React Backend: Node.js or Python Flask AI Model: TensorFlow / PyTorch Database: MySQL / PostgreSQL Image Processing: OpenCV AI Models CNN (Convolutional Neural Network) ResNet50 YOLO for object detection Users Doctors Radiologists Hospitals Patients 2. AI + IoT Lung Monitoring System A smart healthcare platform connected to wearable devices. Features Real-time breathing monitoring Oxygen level tracking AI predicts lung disease risk Emergency alerts Mobile app notifications Patient monitoring dashboard Hardware ESP32 Pulse Oximeter Sensor Temperature Sensor AI Fu...
...000-camera architecture with high availability and low latency • Optimize DeepStream pipelines (NvInfer, NvTracker, NVDEC, GStreamer) for stable FPS under heavy load • Deploy an manage CV models on Jetson or equivalent edge devices • Fine-tune YOLO models and export optimized TensorRT engines • Build and improve infrastructure using Docker, Kafka/MQTT, GPU clusters, load balancing, and autoscaling • Improve real-time analytics dashboards and monitoring systems • Document deployment workflows and architecture decisions Required Skills: • NVIDIA DeepStream SDK • YOLO v5/v8/v9 • TensorRT, ONNX, TAO Toolkit • Jetson Nano/Orin • Docker, Kafka/MQTT, Nginx • RTSP, ONVIF, H.264/H.265 • Python and C++ Experience Req...
I have a growing library of MP4 recordings of casino-grade slot machines and I need a reliable way to turn each session into structured data. For every spin I want the script to capture the start time, end time, bet size, win amount, any bonus triggers, and jackpots, then tally an overall spin count. The video...example command that reproduces your results on my sample MP4s Acceptance criteria • Works on at least three different game layouts without manual retuning • ≥99.95 % accuracy on spin count, bet size and win amount when compared with my hand-labeled ground truth • Correctly flags 100 % of visually distinct bonus/jackpot events in the provided test set Feel free to use OpenCV, Tesseract, YOLO, or any modern deep-learning framework—whatever achi...
...timeout → auto ambulance call with GPS location + live camera image. --- CANCEL WINDOW: Via: app button / numeric code / voice codeword / voice recognition (POST /api/alarm/cancel). Time window configurable per alarm level. No cancel → auto escalation to emergency services. --- FALSE ALARM SUPPRESSION: - Steam in bathroom: ignore smoke trigger if humidity > 80% + person present - Pets: YOLO class filter — only humans trigger intrusion alarm - Battery beeping: audio pattern match, suppress + log for morning report - Audio classification: glass breaking, water sounds, smoke detector patterns --- PRIVACY ZONES (must be respected): - Bathroom: no cameras ever. Radar sensor only (fall detection). - Bedroom: cameras with physical shutter, def...
I am looking for an expert to develop a high-performance, 8-channel automated farming system for the 3D tactical shooter Delta Force. My requirements: Technical Skills & Experience: - Proven experience in real-time computer vision (YOLO, TensorRT) with batch inference and low latency on RTX 4070Ti/4060Ti GPUs. - Strong proficiency in C++ (or high-performance Python with CUDA/C++ backend) and PCIe capture card integration. - Familiarity with KMBOX Net HID-level mouse/touch emulation and anti-cheat evasion (Tencent ACE or similar). - Experience designing centralized dashboards and robust fail-safe management for 24/7 operations. Core Deliverables: - Real-time detection (loot, enemies, extraction points) and UI state recognition via 8x 1080p 60Hz streams. - Visual navigation usi...
...from double-bag events and still keep a reliable tally. Boxes and tins also come through the same point, each in more than one size, so the detection logic has to cope with differing dimensions rather than relying on a fixed template. Here’s the workflow I have in mind: • Your application pulls the RTSP/HTTP stream from my existing IP cameras. • A computer-vision model (OpenCV, TensorFlow, YOLO or similar) detects the item type, counts it, and determines the direction of movement—onto or off the truck. • Counts are logged with time-stamps and can be viewed on a simple web dashboard and exported as CSV. • If the camera loses connection or the count confidence falls below a threshold, the system raises an on-screen alert so we can check manu...
I need a robust YOLO model that spots vehicles with high accuracy. Because I do not yet have a labeled dataset, the job begins with sourcing or capturing varied vehicle images and annotating them in classic YOLO format. Once the dataset is in place, the next step is to train and validate the network—YOLOv5 or YOLOv8 are both fine as long as the final mAP holds up under real-world conditions. Please apply best-practice augmentation, tune hyper-parameters, and track training with clear metrics so I can reproduce the results later in PyTorch. Deliverables • Curated and fully annotated vehicle image dataset (bounding-box labels in YOLO txt format) • Trained YOLO weights and configuration files • Short report summarising dataset compositio...
I have a fixed-angle camera that watches every truck as it rolls through a single gate into our yard. What I need is a reliable, end-to-end Python pipeline that will: • Detect and track each individual truck in real time with a pre-trained YOLO model, keeping the ID stable from the moment the vehicle enters the frame until it leaves. • Within that per-truck track, run additional YOLO passes (or custom classes) to locate the key regions I care about: license plates, DOT numbers, chassis numbers, container numbers, and container type markings. Accuracy on these regions is critical; false positives must be minimal. • Crop each detected region, perform OCR, and then clean the raw text with solid post-processing logic—regex, checksums, or any heuristic that...
...detection 3. Character segmentation and OCR 4. Output structured data: * Vehicle number * Timestamp * Image snapshot 5. Support for: * Day and night conditions * Motion blur handling * Non-standard Indian number plates 6. Offline operation (no cloud dependency) Technical Requirements:** * Strong experience in Computer Vision and Deep Learning * Hands-on experience with: * YOLO / SSD (object detection) * OCR models (CRNN, LPRNet, Tesseract improvements) * Experience with: * OpenCV * PyTorch / TensorFlow * RTSP stream handling (FFmpeg / GStreamer preferred) * Experience deploying models on: * Linux systems * Edge devices (Jetson preferred) --- **SDK Requirements:** * Deliverable must be a reusable SDK (not just an application) * Provide APIs ...
...build a ChatGPT-style application that works exclusively with images and imposes no built-in content filters. The core capability is image analysis, specifically object detection and recognition, with an immediate focus on identifying people and faces in any photo a user uploads. You’re free to choose the tech stack, but I expect modern computer-vision frameworks—think PyTorch, TensorFlow, or a YOLO-family model—backed by a concise, well-documented API so the system can later expand into other tasks such as classification or captioning if I choose. Fast, server-side inference is important; cloud GPU deployment or an optimized on-prem setup is acceptable as long as latency stays low. Please include: • A clean front-end where users drop an image and i...
...self-contained Python program that opens my webcam, runs a YOLO pre-trained model, and draws labeled bounding boxes around the usual everyday items—person, bottle, mobile phone, etc.—whenever they appear in the frame. Please write it in clear, well-commented code that leans on OpenCV (cv2) for video capture and display. You’re free to pull the model weights from any reliable public source as long as setup remains simple (a short and one-step download script are perfect). Deliverables • A runnable demo script that starts the webcam and shows real-time detection • All source files, neatly organised, including a brief README explaining environment setup, how to launch the app, and where to place or fetch the YOLO weights • A short...
...This is purely for demonstration and learning, so the implementation should stay clean and easy to read, ideally relying on OpenCV together with a pre-trained YOLO model (v5, v8, or any recent weight file you are comfortable with). How the app should behave • Load a local MP4 (or similar) sample video. • Run real-time inference frame-by-frame, highlighting every detected person or mobile phone. • Display the processed video in a simple desktop window; no fancy UI is needed beyond the live frame and the FPS readout. • Keep all dependencies to standard libraries plus OpenCV, torch/onnxruntime (if required for your chosen YOLO flavour), and any lightweight helper you feel is essential. Because I only need a basic overview of how it works, a conci...
...am looking for an experienced Computer Vision / AI developer to build a real-time video processing prototype. Scope of work (Phase 1): - Connect to RTSP stream (IP camera / NVR) - Process live video feed - Implement human detection using a lightweight model (e.g., YOLO or similar) - Display bounding boxes on detected objects - Ensure near real-time performance (minimum 8–10 FPS) Technical requirements: - 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) Deliverables: - Working demo (video or live screen) - Source code - Setup instructions...
...clips that isolate each participant’s key moments. • Overlay a configurable sponsor logo on every generated clip. • Add seamless WhatsApp integration so the system can push the personalised clips straight to athletes or organisers. • Clean up, document and hand over the code so the next developer—or I—can maintain it easily. Tech context The previous developer used Python with OpenCV and a YOLO-style model; feel free to refactor or swap frameworks as long as inference remains lightning fast. Expect video streams up to 4K, multiple concurrent cameras and fairly busy backgrounds. Success criteria • ≥95 % accurate bib recognition measured on my test footage. • Clip creation and logo overlay processed within 30 s of finish-line ...
...Output Clean, editable AutoCAD DWG file containing: Correctly placed blocks for all detected casework, sinks, fixtures, shelving, etc. Accurate dimensions (height, width, spacing) Countertop outlines with detailed top-view information Section views based on cabinet types Proper layers, line types, and drafting standards Visual flags / notes for any unmatched or uncertain items Core Features YOLO-based (or equivalent) object detection for cabinets, sinks, pegboards, etc. Vision LLM assistance for annotation and context understanding Block matching engine with fallback logic Natural language rule engine (“teach” the AI in plain English) Self-training interface (view, edit, enable/disable rules) Drawing-based learning (compare AI DWG vs. user-corrected DWG) Confidenc...
...detection 3. Character segmentation and OCR 4. Output structured data: * Vehicle number * Timestamp * Image snapshot 5. Support for: * Day and night conditions * Motion blur handling * Non-standard Indian number plates 6. Offline operation (no cloud dependency) Technical Requirements:** * Strong experience in Computer Vision and Deep Learning * Hands-on experience with: * YOLO / SSD (object detection) * OCR models (CRNN, LPRNet, Tesseract improvements) * Experience with: * OpenCV * PyTorch / TensorFlow * RTSP stream handling (FFmpeg / GStreamer preferred) * Experience deploying models on: * Linux systems * Edge devices (Jetson preferred) --- **SDK Requirements:** * Deliverable must be a reusable SDK (not just an application) * Provide APIs ...
...User guide for running the system • Instructions for adjusting output styles and using custom backgrounds 5. Source Files • Complete source code • Trained models (if custom training is involved) • Configuration templates Ideal Candidate Profile We're looking for someone with: Required Skills: • Strong experience with AI/ML video processing • Expertise in computer vision (OpenCV, MediaPipe, YOLO) • Knowledge of generative AI models for video (Stable Diffusion, ControlNet, AnimateDiff, or similar) • Proficiency in Python and video processing libraries (MoviePy, FFmpeg, PyTorch/TensorFlow) • Experience with style transfer or video-to-video translation • Experience with video compositing and background replacement techniques...
...plug-and-play as possible. When a new football pitch is added: 1. Cameras are connected 2. The local computer is configured 3. The software is installed 4. Data automatically syncs to the main system --- # Technical Requirements The freelancer should have proven experience with: * Computer Vision * Multi-object tracking * Re-identification (ReID) * DeepSORT, ByteTrack, BoTSORT or similar systems * YOLO, Detectron or similar detection models * Multi-camera synchronization * Cross-camera player tracking * Real-time video processing * GPU optimization * Web panel development * Backend and database development * API development Preferred experience: * Sports analytics * Football / basketball tracking projects * NVIDIA GPU optimization * Multi-camera systems Please share: * ...
I need a rock-solid, real-time player tracking module for football matches that guarantees the ID assigned to each athlete at kick-off never changes until the final whistle. Right now, our OpenCV–TensorFlow–YOLO pipeline sometimes swaps or loses IDs when athletes overlap, leave the frame briefly, or the camera angle shifts, and that ruins every speed, distance, position, and heat-map metric we generate. Key requirements • Sport: football. • Camera setup: five or more synchronized feeds. • Existing stack: OpenCV, TensorFlow, YOLO – your solution must plug into this environment. What I expect 1. A multi-object tracker with integrated re-identification that preserves the same unique ID through occlusion, crossings, short disappearances, or ...
...compares the performance of today’s most widely cited object-detection algorithms. The focus is strictly on Computer Vision, zeroing in on Object Detection, and the core goal is to evaluate how the main approaches stack up against each other in terms of accuracy, speed, computational cost, and real-world suitability. Scope • Analyse at least three state-of-the-art methods—think Faster R-CNN, SSD, YOLO (v7/8), DETR or similar. • Draw all claims from peer-reviewed journals, top-tier conference papers, or authoritative benchmark leaderboards (e.g., COCO, PASCAL VOC). • Present metrics consistently (mAP, FPS, FLOPs, params, latency) so direct comparison is effortless. • Highlight strengths, weaknesses, and trade-offs instead of simply listing ...
...compares the performance of today’s most widely cited object-detection algorithms. The focus is strictly on Computer Vision, zeroing in on Object Detection, and the core goal is to evaluate how the main approaches stack up against each other in terms of accuracy, speed, computational cost, and real-world suitability. Scope • Analyse at least three state-of-the-art methods—think Faster R-CNN, SSD, YOLO (v7/8), DETR or similar. • Draw all claims from peer-reviewed journals, top-tier conference papers, or authoritative benchmark leaderboards (e.g., COCO, PASCAL VOC). • Present metrics consistently (mAP, FPS, FLOPs, params, latency) so direct comparison is effortless. • Highlight strengths, weaknesses, and trade-offs instead of simply listing ...
