Performance Of Video Surveillance System Research Articles

가중치 정보를 이용한 영상 분할 알고리즘

영상 감시 시스템에서 핵심적인 기술은 얼마나 정확하게 검출을 할 수 있는가 하는 것이다. 객체를 정확하게 추적하기 위해서는 영상에서 배경과 객체를 정확히 분리 할 수 있어야 한다. 하지만 사람의 시각이 아닌 시스템 스스로가 배경과 객체를 정확히 구분하고, 상황을 판단하는 것은 쉽지 않다. 영상처리 시스템의 신뢰도를 높이기 위해서는 배경과 객체를 정확히 검출해 내야 객체를 정확하게 추적할 수 있게 되며 이는 시스템 제작의 성공 여부에 큰 영향을 끼친다. 본 논문은 시시각각으로 변하는 배경 환경을 정확히 파악하기 위해 배경의 변화를 관찰하여 배경과 객체를 보다 정확하게 구분할 수 있는 방안을 제안하였다. The most important and critical to the performance of video surveillance systems is to be detected exactly how much. In order to accurately track the object must be able to accurately separate the background and object. However, the system itself rather than the human vision exactly distinguish the object and the background, to assess the situation, it is not easy. If we can accurately detect the background and the object, to be able to accurately track an object, it is possible to increase the reliability of the system, have a significant impact on the success of the entire production system. In this paper, we propose a way to distinguish more precisely the background and the object being to determine the background environment changes more accurately.

A Review and Comparison of Measures for Automatic Video Surveillance Systems

Today's video surveillance systems are increasingly equipped with video content analysis for a great variety of applications. However, reliability and robustness of video content analysis algorithms remain an issue. They have to be measured against ground truth data in order to quantify the performance and advancements of new algorithms. Therefore, a variety of measures have been proposed in the literature, but there has neither been a systematic overview nor an evaluation of measures for specific video analysis tasks yet. This paper provides a systematic review of measures and compares their effectiveness for specific aspects, such as segmentation, tracking, and event detection. Focus is drawn on details like normalization issues, robustness, and representativeness. A software framework is introduced for continuously evaluating and documenting the performance of video surveillance systems. Based on many years of experience, a new set of representative measures is proposed as a fundamental part of an evaluation framework.

An advanced video-based system for monitoring active volcanoes

Video surveillance systems are consolidated techniques for monitoring eruptive phenomena in volcanic areas. Along with these systems, which use standard video cameras, people working in this field sometimes make use of infrared cameras providing useful information about the thermal evolution of eruptions. Real-time analysis of the acquired frames is required, along with image storing, to analyze and classify the activity of volcanoes. Human effort and large storing capabilities are hence required to perform monitoring tasks. In this paper we present a new strategy aimed at improving the performance of video surveillance systems in terms of human-independent image processing and storing optimization. The proposed methodology is based on real-time thermo-graphic analysis of the area considered. The analysis is performed by processing images acquired with an IR camera and extracting information about meaningful volcanic events. Two software tools were developed. The first provides information about the activity being monitored and automatically adapts the image storing rate. The second tool automatically produces useful information about the eruptive activity encompassed by a selected frame sequence. The software developed includes a suitable user interface allowing for convenient management of the acquired images and easy access to information about the volcanic activity monitored.