Object tracking vs Object detection- a comparative analysis

anand zutshi (~anand09)


2

Votes

Description:

In computer vision, object detection is scanning and searching for an object in an image or a video (which is just sequence of images). Whereas, object tracking is like you are spying on someone and following it. Done in motion images like in animated gifs or videos, we want to track how an object is moving, where is it going, or its speed.

Although it has been studied for dozens of years, object detection and tracking remains an open research problem. The difficulty level of this problem highly depends on how you define the object to be detected and tracked. If only a few visual features, such as a specific color, are used as representation of an object, it is fairly easy to identify all pixels with same color as the object. On the other extremity, the face of a specific person, which full of perceptual details and interfering information such as different poses and illumination, is very hard to be accurately detected, recognized and tracked.

Thus, I believe it is important to address such challenges via a comparative study of object tracking and object detection in python. Here, I aim to present my own experience in tackling the problems while I tested different algorithms for the same.

Prerequisites:

Basic understanding of python

Content URLs:

(Slides to be uploaded soon)

Speaker Info:

Anand Zutshi is currently pursuing his undergraduate B.E. degree from Netaji Subhas Institute Of Technology, Delhi. He has experience in developing and testing basic as well as advanced algorithms in C, C++. He has experience in developing a Learning Management System which uses dynamically trained neural network for scoring its users, and a LDA based tagging in its queries. He has in depth knowledge of Natural Language Processing, mainly with emphasis on word sense disambiguation and language models. His recent work of interest primarily focusses on object detection and object tracking in Python and sound classification and recognition.

Currently, he is working on testing a biometric database management system along with predicting self and non-self processes in Operating system using Neural Networks.

Speaker Links:

https://github.com/zutshianand

Id: 698
Section: Data science
Type: Talks
Target Audience: Beginner
Last Updated: