Guest EditorialIntelligent video surveillance in crowded scenes
Introduction
With the rapidly growing of the Internet and storage capacity, IP-based video monitoring systems have become popular applications. As the network video technology has improved, the cost of installing a surveillance system has dropped significantly, leading to an exponential increase in the use of security cameras. Traditional CCTV technologies require a large number of human operators to continuously monitor surveillance cameras. On the contrary, intelligent surveillance systems require much fewer human operators because they can provide automated services, such as intrusion detection, robbery prevention, population (people) counting, and loitering detection. One of the most challenging tasks in such intelligent surveillance systems is visual surveillance in crowded and complicated scenes. Such systems should be robust and adaptable enough to effectively detect changes in the environment such as lighting, scene geometry or scene activities. However, most existing work in activity and scene analyses are not effective for the task of detecting changes in crowded and complicated scenes.
This special issue aims at putting together recent advances in visual surveillance applications which explicitly demonstrate the concepts and techniques spanning the areas of computer vision, pattern analysis, imaging sensors, computational intelligence, algorithmic development and system architecture. Each manuscript was blindly reviewed by at least three reviewers consisting of guest editors and external reviewers. After all review processes, four manuscripts were finally selected to be included in this special issue. We briefly summarize these manuscripts in the following section.
Section snippets
Descriptions of the manuscripts
In the first manuscript “Towards Crowd Density-Aware Video Surveillance Applications,” Fradi and Dugelay [1] propose an approach for determining the crowd density, in which local information at the pixel level substitutes a global crowd level or a number of people per-frame. Their approach consists of the following three methods: (1) applying a scene-adaptive dynamic parameterization using the crowd density measure; (2) analyzing temporal and spatial distributions of people using long-term
Acknowledgement
Our special thanks go to Prof. Francisco Herrera and all editorial staff for their tremendous support throughout the preparation and publication of this special issue. We would like to thank all authors for their contributions to this special issue. We also extend our thanks to the external reviewers for their time and efforts in reviewing the manuscripts.
References (4)
- et al.
Towards crowd density-aware video surveillance applications
Inform. Fus.
(2015) - et al.
Saliency-directed prioritization of visual data in wireless surveillance networks
Inform. Fus.
(2015)
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