A self automatically labeling tool
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Updated
Feb 2, 2018 - Python
A self automatically labeling tool
CNN + OpenCV based perimeter control system
Compare FasterRCNN,Yolo,SSD model with the same dataset
MobileNet-SSD(MobileNetSSD) + Neural Compute Stick(NCS) Faster than YoloV2 + Explosion speed by RaspberryPi · Multiple moving object detection with high accuracy.
RaspberryPi3(Raspbian Stretch) + MobileNetv2-SSDLite(Tensorflow/MobileNetv2SSDLite) + RealSense D435 + Tensorflow1.11.0 + without Neural Compute Stick(NCS)
Video Analysis using Machine Learning
Edge TPU Accelerator / Multi-TPU + MobileNet-SSD v2 + Python + Async + LattePandaAlpha/RaspberryPi3/LaptopPC
[High Performance / MAX 30 FPS] RaspberryPi3(RaspberryPi/Raspbian Stretch) or Ubuntu + Multi Neural Compute Stick(NCS/NCS2) + RealSense D435(or USB Camera or PiCamera) + MobileNet-SSD(MobileNetSSD) + Background Multi-transparent(Simple multi-class segmentation) + FaceDetection + MultiGraph + MultiProcessing + MultiClustering
using ncnn, on android, detection, model size only 11MB, 10fps on 845/cpu
It is a social distance detector based on OpenCV and YOLOV3 / Mobilenet_SSD used to find track persons who are following social distance and who are not following.
This repository implements object detection using YOLO and MobileNetSSD techniques in real-time. Both techniques use OpenCv to access the webcam.
Artificial intelligence powered real-time video analytics software for retail stores
Using Pre-Trained Model to recognise images
Social Distancing Confirming using OpenCV Python
👁️🗨️ PWA for visually impaired people that announces objects detected with user's phone camera.
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