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

  1. Build a Face Detector Algorithm to capture faces through webcam using OpenCV.

  2. You'll learn how a computer learns to see the world and recognizes them.

  3. 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

Id: 1216
Section: Data Science, Machine Learning and AI
Type: Workshop
Target Audience: Intermediate
Last Updated: