Evolution of Object Detection Algorithms
K Kk (~k05) |
Description:
An overview of evolution of all object detection algorithms like SIFT, HOG, OverFeat, RCNN, Faster RCNN , YOLO etc. A topic on how object detection works and giving explanation to all advances and improvements in modern object detection algorithms and what is the current area of research in on-going object detection. Giving Hands-on in sliding window, image-pyramids etc.
Take aways:
By the end of the workshop
Build a Face Detector Algorithm to capture faces through webcam using OpenCV.
You'll learn how a computer learns to see the world and recognizes them.
You'll learn how Deep Learning works and see how a Pretrained YOLO works .
Duration : 150 min.
Prerequisites:
Beginner programming level in python. An interest is a MUST.
Speaker Info:
I'm a Computer Vision Researcher. Previously, I worked as Machine Learning Researcher and I'm also a graduate of Udacity's Computer Vision Nanodegree. My research areas are specifically in Object Detection and Object Classification.
Speaker Links:
My Github Link : https://github.com/karankumar-07