Vijay Yadav's starred repositories
face.evoLVe
🔥🔥High-Performance Face Recognition Library on PaddlePaddle & PyTorch🔥🔥
kalman-cpp
Basic Kalman filter implementation in C++ using Eigen
opencv-face-recognition-python
Face Recognition using OpenCV and Python.
MobileNet-SSD-RealSense
[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
FaceRecognition
Face Recognition using OpenCV in Python
OpenVINO-EmotionRecognition
OpenVINO+NCS2/NCS+MutiModel(FaceDetection, EmotionRecognition)+MultiStick+MultiProcess+MultiThread+USB Camera/PiCamera. RaspberryPi 3 compatible. Async.
kalman-object-tracking
Kalman filtering-based visual object tracking. Originally was used for faces, but can be used with any rectangular objects.
openvino_example
this is for intel openvino (https://software.intel.com/en-us/openvino-toolkit)
Object-and-facial-detection-in-python
This repo contains, training material, dlib implementation, tensorflow implementation and an own made complete system implementation with a parse-controller.
intel-iot-devkit-people-counter
This people counter application is one of a series of IoT reference implementations aimed at instructing users on how to develop a working solution for a particular problem. It demonstrates how to create a smart video IoT solution using Intel® hardware and software tools. This people counter solution detects people in a designated area providing number of people in the frame, average duration of people in frame, and total count.