Abstract
In spite of excellent performance of deep learning-based computer vision algorithms, they are not suitable for real-time surveillance to detect abnormal behavior because of very high computational complexity. In this paper, we propose a real-time surveillance system for abnormal behavior analysis in a closed-circuit television (CCTV) environment by constructing an algorithm and system optimized for a CCTV environment. The proposed method combines pedestrian detection and tracking to extract pedestrian information in real-time, and detects abnormal behaviors such as intrusion, loitering, fall-down, and violence. To analyze an abnormal behavior, it first determines intrusion/loitering through the coordinates of an object and then determines fall-down/violence based on the behavior pattern of the object. The performance of the proposed method is evaluated using an intelligent CCTV data set distributed by Korea Internet and Security Agency (KISA).
Highlights
Recent advances in deep learning technology have made a quantum leap in computer vision-based analysis of abnormal behavior in a circuit television (CCTV) environment
Various action recognition methods were proposed for abnormal behavior analysis in the literature, there are a few CCTV-based surveillance systems for analyzing abnormal behavior based on the identification of the same object
Since abnormal behavior analysis should work with real-time streaming images as input, object detection does not perform at every frame, and a tracking algorithm estimates the coordinate of the object in the skipped frames
Summary
Recent advances in deep learning technology have made a quantum leap in computer vision-based analysis of abnormal behavior in a CCTV environment. Vision-based surveillance techniques try to detect and classify intrusion, loitering, fall-down, and violence, to name a few. Various action recognition methods were proposed for abnormal behavior analysis in the literature, there are a few CCTV-based surveillance systems for analyzing abnormal behavior based on the identification of the same object. We proposed a novel abnormal behavior analysis method in a CCTV environment by merging detection, tracking, and action recognition algorithms. Pedestrian detection and tracking method is used in a module for identifying the location of a pedestrian, and an intruding or loitering pedestrian in a specific area can be detected using the coordinates of the tracked pedestrian In this module, pedestrian information is stored to analyze the abnormal behavior of the same pedestrian, and the image of the pedestrian is transmitted to the abnormal behavior analysis module through.
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