HUMAN ACTIVITY RECOGNITION MODEL AS A VIOLENCE DETECTION SYSTEM
In an era when many CCTVs were installed to monitor activities in public places, these CCTVs could certainly provide a sense of security because of supervision by the authorities. However, monitoring becomes difficult to do considering there are so many locations that need to be monitored cont...
Saved in:
| Main Author: | |
|---|---|
| Format: | Theses |
| Language: | Indonesia |
| Online Access: | https://digilib.itb.ac.id/gdl/view/55948 |
| Tags: |
Add Tag
No Tags, Be the first to tag this record!
|
| Institution: | Institut Teknologi Bandung |
| Language: | Indonesia |
| Summary: | In an era when many CCTVs were installed to monitor activities in public places,
these CCTVs could certainly provide a sense of security because of supervision by
the authorities. However, monitoring becomes difficult to do considering there are
so many locations that need to be monitored continuously. To facilitate this, a
system for detecting criminal activity, especially in the case of violence, could be
used to assist surveillance. A spatiotemporal action localization model is a model
that can be used to detect and warn authorities of violent action caught by CCTV.
This research focuses on creating a model that can be the basis for the development
of an efficient physical violence detection system. Efficiency becomes especially
important when a model is to be applied to process many videos produced from
many CCTVs. Using efficient three-dimensional convolution techniques, the use of
memory resources can be minimized while maintaining decent accuracy. The model
also uses multiple-scale prediction techniques that can improve the model's ability
to detect small objects, such as firearms and knives. Experimental results with the
UCF101-24 dataset show that the model achieves a frame-mAP score of 67.29%
using only 22.84 million parameters.
|
|---|