Security Event Recognition for Visual Surveillance

TL;DR

This paper introduces a CNN-based framework for automatic security event recognition in surveillance videos, capable of detecting objects, recognizing owners, and distinguishing between theft and benign movements, validated on a new complex dataset.

Contribution

The paper presents a novel CNN-based framework that integrates object detection, owner recognition, and event analysis for security in surveillance videos, outperforming existing methods.

Findings

Outperforms state-of-the-art methods in security event recognition

Effective in complex scenarios with various security events

Validated on a newly constructed, diverse video dataset

Abstract

With rapidly increasing deployment of surveillance cameras, the reliable methods for automatically analyzing the surveillance video and recognizing special events are demanded by different practical applications. This paper proposes a novel effective framework for security event analysis in surveillance videos. First, convolutional neural network (CNN) framework is used to detect objects of interest in the given videos. Second, the owners of the objects are recognized and monitored in real-time as well. If anyone moves any object, this person will be verified whether he/she is its owner. If not, this event will be further analyzed and distinguished between two different scenes: moving the object away or stealing it. To validate the proposed approach, a new video dataset consisting of various scenarios is constructed for more complex tasks. For comparison purpose, the experiments are also carried out on the benchmark databases related to the task on abandoned luggage detection. The experimental results show that the proposed approach outperforms the state-of-the-art methods and effective in recognizing complex security events.