Abstract:
Nowadays the world become a digitalization. That's why now security problems are rising
every day. We have too much concern about the security system. So human activity
detection’s significance is also increased argent. Human activity detection (HAD) is a
significant series of data in the computer vision community. Nowadays we are utilizing
CCTV cameras, Smartphone cameras for reliability plan. Human action acknowledgment
is worried about recognizing various kinds of human developments and activities utilizing
information accumulated from different ways. In this project report, we are proposing an
improved DCNN that can recognize eating, walking, working, playing, fighting, and other
types of real-time human activity are being from images. Using the Deep Convolutional
Neural Network model (DCNN) images were fed for image classification. Then, by means
of joining the DCNN model with a custom human action identification dataset, limits and
new attaching is one great step. The benefit of an improved Deep Convolution Neural
Network or (DCNN) is its capacity to separate attributes from the data. Transfer Learning
was used to feature extract the images and also the methodology we used is transfer
learning. Used Keras framework to train the images. In our Project, we used Keras and
TensorFlow as a framework. Moreover, we have contrasted the further improved DCNN
model and other conventional techniques, and here the improved DCNN model
accomplished an accuracy pace of 98.82% and outperforms different models.
