Object detection + tracking + counting using Ultralytics YOLO26... 🚀 👉 Object detection tells you what is in a scene. 👉 Tracking follows each object across video frames. 👉 Counting turns those movements into actionable data. YOLO26 delivers fast, accurate detections, while multi-object tracking maintains unique identities across frames. Add configurable counting lines or regions to measure entries, exits, directional movement, and other real-world traffic metrics. #vehicles #MachineLearning #Highwayshow more

Object tracking with FastSAM using Ultralytics Python package 🎉💪... FASTSAM-s model is employed for real-time object detection, combined with botsort object tracking to further boost performance. 🐻 Code link 👇 #FastSAM #MachineLearning #Researchshow more

Football players detection + tracking using Ultralytics YOLO26 ⚽️... It took me 30 minutes to train the YOLO26 model on a custom dataset using Ultralytics Platform. Later, I used the object tracker (Bytetrack) for tracking the players; tracking could be improved further, and I am working on it this week. More info 👇 #soccer #football #MachineLearningshow more

New trackers released by Ultralytics 😍 botsort, bytetrack, ocsort,... deepocsort, fasttrack, and tracktrack. Object tracking tells you which object is which across frames. I recently tested the newly released trackers in this demo. Here's what I found. 👇 #tracker #MachineLearning #Researchshow more

People counting in zones using Ultralytics YOLO26 🧑‍🤝‍🧑 In... this demo, people are moving on elevators. I used a custom-trained YOLO26 model to detect and track each person, then applied the object counting in zones solution to count the number of people within each zone. 🙂 #people #retail #MachineLearningshow more

Vehicle tracking using Ultralytics YOLO26 👀 In this demo,... I trained the YOLO26 model on a custom dataset and later used an object tracker (Bytetrack) to track the objects in the video file. Note: I also performed testing with Botsort, which is also a good tracker, but it's slow in comparision to Bytetrack. #Tracking #ComputerVision #MachineLearningshow more

Detect worker falls in real time with Ultralytics YOLO26!... 📦 Combine object detection and pose estimation to identify when a person carrying luggage boxes falls, helping logistics teams improve warehouse safety and respond faster to incidents. Get started ➡️ #Ultralytics #YOLO26 #AI #Logistics #WorkplaceSafetyshow more

Object Detection + Tracking (ID + Trails) using YOLOv8... on custom dataset. Github Repo: Youtube Video Tutorial:show more

Real-time bakery item counting using Ultralytics YOLO26 😍 In... food production lines, counting sounds simple until it isn’t. Items move fast, overlap on conveyors, change orientation, and sometimes partially occlude each other. Whether it’s ice-cream cones on a belt or cream being filled into nests, maintaining accurate counts in real time is critical for quality control and throughput. More info 👇 #Bakery #Retail #MachineLearningshow more

Animals segmentation & tracking using Ultralytics YOLO26 🫎 How... I created this demo: - Data annotation using Ultralytics Platform. - YOLO26 model training using Platform. - Downloaded the trained model. - Run Inference on video files locally. #animal #ai #MachineLearningshow more

Multi-Object Marker Tracking

RoadScan-AI-Automated-Pothole-Detection-Tracking RoadScan AI is a computer vision system built... to automatically detect and track potholes in road footage — including dashcam, drone, and fixed-camera video. At its core, the system uses a YOLO11n model fine-tuned on a custom pothole dataset, paired with ByteTrack for multi-object tracking. This enables the system to maintain a consistent identity for each pothole across frames rather than treating every detection as a new, isolated event.show more

Launching today: Ultralytics YOLO26! The fastest, most deployable YOLO... ever built. For years, shipping object detection meant extra steps and messy post-processing. YOLO26 changes the game with end-to-end, NMS-free inference, simpler deployment, and strikingly low latency. Built End-to-End. Built for the Edge. 👉 Get started today:show more

Smart parking pipeline in real-time with Ultralytics YOLO26 🚗... 🅿️ Over the weekend, I built a parking management system that monitors slot occupancy (occupied vs available). In busy parking areas where vehicles partially overlap slots or enter at angles, the system still maintains accurate detection and status updates in real time. More information 👇 #yolo26 #parkingproblem #machinelearningshow more

Real-time object detection will never be the same. Traditional... YOLO needs NMS to remove duplicate boxes; it's slow and inconsistent. YOLO26 skips it entirely: single-pass predictions, faster inference and up to 300 detections per image. Download model:show more

introducing: fruit xylophone a computer vision music experiment built... with mediapipe hand tracking and roboflow RF-DETR object detection (running in realtime in the browser using transformers js)show more

SAM3 video tracking is so good yesterday: collect data,... train custom object detector, use tracker to estimate object motion - days today: track anything with text prompt - secondsshow more

smart parking demo - RF-DETR - license plate detection... - OC-SORT - multi-object tracking - GLM-OCR - reading license plates on Monday I showed you trackers, yesterday GLM-OCR, today I'm putting everything togethershow more

Count rooftop panels with Ultralytics YOLO26 OBB! 🏠 Use... oriented bounding boxes to detect solar panels from aerial views and capture their orientation, enabling accurate counting for inspections, asset management, and infrastructure analytics. Explore more ➡️ #Ultralytics #YOLO26 #AI #ObjectCounting #ComputerVisionshow more

Meta's SAM 3 is a beast. before: collect data,... train custom object detector, use tracker to estimate object motion - days now: track anything with text prompt - seconds real-time object detection will never be the same.show more

Pothole detection on the road in real time using... Ultralytics YOLO26! 🕳️ Manual road inspections are slow, costly, and hard to scale. With object detection, potholes can be identified directly from street-level images or video feeds, enabling faster and more consistent road condition monitoring. How I built this demo: ✅ Trained a segmentation model on a custom dataset. ✅ Generated mask contours for each pothole. ✅ Leveraged the onnx-exported model for faster processing. #Pothole #RoadDamage #AIshow more