Abstract:
Video classification is a project in which we work on videos. As videos are a group of
images in a specific order. In this project we recognize pictures action perform in a
video. This helps to minimize the human effort to classify Videos in a different tag.
We using python which minimizes the code and generates the maximum output. Using
CNNs (Convolutional Neural Network) to extract images from the Videos that we train
it by applying training and evaluation models to evaluate our work. CNN's is used as
a feature Extractor which extracts image features and then classifies these images on
an appropriate tag.
The objective of this project is to develop Video Classification Application to
classify video. This report explores different techniques used for the classification of
videos. Different stages involving image processing like the preprocessing stage,
segmentation, and feature extraction will be studied and discussed. Finally, the end
product of the algorithms will be written in the software called PyCharm.
This project uses the Artificial Neural Network technique to develop the
Application. The main advantage of using this technique is that it provides features
extraction and then train and evaluate the Model
