This document proposes using a pre-trained 3D convolutional neural network (CNN), named C3D, for violence detection in videos captured by cameras. The approach involves detecting persons in the video stream and processing only relevant frames to improve efficiency, addressing the challenges of identifying complex visual patterns. The goal is to enhance safety by accurately detecting violence and abnormal activities to protect individuals from threats such as harassment.

1. ENHANCED PROTECTION
USING CNN

2. Introduction
In this work, we propose the use of an existing pre-trained 3D
Convolutional Neural Network (CNN), named C3D . Here we use
CNN for violence detection in videos through cameras and going
to implement next move to protect us .

3. 3D CNN can understand the movement over time in
videos. It does this by using 3D convolution and
pooling. This means it looks at groups of frames
stacked together to figure out how things change
over time
3D CNN :

4. Figure of Convolution and pooling
Figure of frame detection in videos

5. VIOLENCE DETECTION :
Detecting violence in video data presents a significant challenge due
to the intricate identification of complex sequential visual patterns.
There are many unimportant frames so it takes more memory .so , we
first detected the persons in the video stream using a pre-trained
CNN model.
Only the sequence of 16 frames containing persons was passed to the
3D CNN model for final prediction, which helped achieve effiecient
processing.

6. ABNORMAL ACTIVITY: NORMAL ACTIVITY:

7. OUR IDEA OF IMPLEMENTATION :
 It is impossible to detect every violence through cctv camers
Accurately.
 By implementing our idea through cam specs we can able
detect every video frames accurately using 3D CNN.
 And it’s easy to protect us from major threads like harassment
And informal attacks.
 Hope ,Even it works better than Kavalan SOS which is implemented by
our TN government

9. DATA THROUGH
FRONT END:

10. Thanks!
PRESENTORS:
NAWFAL ARSATH M
PRAKASH RAJ S