Abstract
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.
Highlights
Security at public place has always been one of the most important social topics
The faster region proposal convolution neural network (FrRCNN) is only used within the foreground regions instead of the whole image to reduce computation, which is important for real-time application
We propose a novel framework for security event recognition in surveillance videos which includes abandoned object detection and special event analysis
Summary
With rapidly increasing deployment of surveillance cameras, the reliable methods for automatically analyzing the surveillance videos and recognizing special events are demanded by different practical applications, such as security monitoring (Collins et al, 2000, Liao et al, 2015a), traffic controlling (Wang et al, 2009, Liao et al, 2015b), etc. Due to their large market and practical impact, much attention has been drawn in both computer vision and photogrammetry communities for decades. It is a good choice to use deep learning methods to detect object type in the task of security event recognition.
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