CNNs for Surveillance Footage Scene Classification

09/08/2018
by   Utkarsh Contractor, et al.
0

In this project, we adapt high-performing CNN architectures to differentiate between scenes with and without abandoned luggage. Using frames from two video datasets, we compare the results of training different architectures on each dataset as well as on combining the datasets. We additionally use network visualization techniques to gain insight into what the neural network sees, and the basis of the classification decision. We intend that our results benefit further work in applying CNNs in surveillance and security-related tasks.

READ FULL TEXT
research
06/21/2021

Boggart: Accelerating Retrospective Video Analytics via Model-Agnostic Ingest Processing

Delivering fast responses to retrospective queries on video datasets is ...
research
11/20/2018

Are pre-trained CNNs good feature extractors for anomaly detection in surveillance videos?

Recently, several techniques have been explored to detect unusual behavi...
research
05/26/2021

Receptive Field Regularization Techniques for Audio Classification and Tagging with Deep Convolutional Neural Networks

In this paper, we study the performance of variants of well-known Convol...
research
01/21/2018

Scene recognition with CNNs: objects, scales and dataset bias

Since scenes are composed in part of objects, accurate recognition of sc...
research
06/13/2021

A Fuzzy Post-project Evaluation Approach for Security Video Surveillance System

Video surveillance is an essential component of the public security syst...
research
07/27/2015

Discovery of Shared Semantic Spaces for Multi-Scene Video Query and Summarization

The growing rate of public space CCTV installations has generated a need...
research
06/27/2019

Loss Switching Fusion with Similarity Search for Video Classification

From video streaming to security and surveillance applications, video da...

Please sign up or login with your details

Forgot password? Click here to reset