Intelligent distributed surveillance systems: a review

By M. Valera, Sergio A. Velastin. 2005

801 citations recorded by the source corpus.

Subfields: Computer Vision and Pattern Recognition

View Paper: https://www.semanticscholar.org/paper/ce9643fbfcb9c3156c7b26b5c92ec3bc67111202

How researchers see this paper

The researchers whose trusted networks rank it highest, strongest advocate first. Every researcher gets a different order.

In Computer Vision and Pattern Recognition

1. Tao Mei
2. Xiaogang Wang
3. Wu Liu
4. Yi Yang
5. Xinchen Liu
6. Huadong Ma
7. Chunhua Shen
8. Luc Van Gool

Citations in the corpus

• Understanding Transit Scenes: A Survey on Human Behavior-Recognition Algorithms (2010, background): “However, there is not much work reported on the integration of different types of sensors in automated video surveillance systems [5].”
• Intelligent multi-camera video surveillance: A review (2013, background): “While some existing reviews Valera and Velastin (2004) and Aghajan and Cavallaro (2009) tried to cover all the aspects of 0167-8655/$ -see front matter Ó 2012 Elsevier B.V.”
• A Review on Video-Based Human Activity Recognition (2013, background): “Valera and Velastin [89] survey the automated visual surveillance systems.”
• A Review of Computer Vision Techniques for the Analysis of Urban Traffic (2011, background): “A review of general surveillance systems is provided in [79] and [139], with a particular focus on distributed surveillance systems.”
• Survey on Contemporary Remote Surveillance Systems for Public Safety (2010, background): “The current research of 2GSS rests in real-time robust computer vision algorithms, automatic learning of scene variability and patterns of behavior, and eliminating the differences between the statistical analyses of a scene and establishing natural language interpretations [2].”
• A survey on activity recognition and behavior understanding in video surveillance (2013, neutral): “Valera 05 Intelligent distributed surveillance [187]”
• PROVID: Progressive and Multimodal Vehicle Reidentification for Large-Scale Urban Surveillance (2018, background): “Vehicle Re-Id can be pervasively applied in intelligent surveillance systems [5], smart transportation [6], and urban”
• A Deep Learning-Based Approach to Progressive Vehicle Re-identification for Urban Surveillance (2016, background): “pervasive applications in video surveillance [4], intelligent transportation [5], and urban computing [6], which can quickly discover, locate, and track the target vehicles in large-scale surveillance videos.”