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Journal ArticleDOI

Intelligent distributed surveillance systems: a review

08 Apr 2005-Vol. 152, Iss: 2, pp 192-204
TL;DR: This survey describes the current state-of-the-art in the development of automated visual surveillance systems to provide researchers in the field with a summary of progress achieved to date and to identify areas where further research is needed.
Abstract: This survey describes the current state-of-the-art in the development of automated visual surveillance systems so as to provide researchers in the field with a summary of progress achieved to date and to identify areas where further research is needed. The ability to recognise objects and humans, to describe their actions and interactions from information acquired by sensors is essential for automated visual surveillance. The increasing need for intelligent visual surveillance in commercial, law enforcement and military applications makes automated visual surveillance systems one of the main current application domains in computer vision. The emphasis of this review is on discussion of the creation of intelligent distributed automated surveillance systems. The survey concludes with a discussion of possible future directions.

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Citations
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Journal ArticleDOI
TL;DR: This paper reviews the recent development of relevant technologies from the perspectives of computer vision and pattern recognition, and discusses how to face emerging challenges of intelligent multi-camera video surveillance.

695 citations


Cites background or result from "Intelligent distributed surveillanc..."

  • ...According to the most recent works, some problems can be jointly solved in order to improve the efficiency and accuracy....

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  • ...While some existing reviews Valera and Velastin (2004) and Aghajan and Cavallaro (2009) tried to cover all the aspects of architectures, technologies and applications, this paper emphasizes the connection and integration of these key computer vision and pattern recognition technologies in various…...

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  • ...Many intelligent multi-camera ll rights reserved. video surveillance systems have been developed (Collins et al., 2001; Aghajan and Cavallaro, 2009; Valera and Velastin, 2004)....

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  • ...All rights reserved....

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Journal ArticleDOI
TL;DR: A comprehensive review of the state-of-the-art computer vision for traffic video with a critical analysis and an outlook to future research directions is presented.
Abstract: Automatic video analysis from urban surveillance cameras is a fast-emerging field based on computer vision techniques. We present here a comprehensive review of the state-of-the-art computer vision for traffic video with a critical analysis and an outlook to future research directions. This field is of increasing relevance for intelligent transport systems (ITSs). The decreasing hardware cost and, therefore, the increasing deployment of cameras have opened a wide application field for video analytics. Several monitoring objectives such as congestion, traffic rule violation, and vehicle interaction can be targeted using cameras that were typically originally installed for human operators. Systems for the detection and classification of vehicles on highways have successfully been using classical visual surveillance techniques such as background estimation and motion tracking for some time. The urban domain is more challenging with respect to traffic density, lower camera angles that lead to a high degree of occlusion, and the variety of road users. Methods from object categorization and 3-D modeling have inspired more advanced techniques to tackle these challenges. There is no commonly used data set or benchmark challenge, which makes the direct comparison of the proposed algorithms difficult. In addition, evaluation under challenging weather conditions (e.g., rain, fog, and darkness) would be desirable but is rarely performed. Future work should be directed toward robust combined detectors and classifiers for all road users, with a focus on realistic conditions during evaluation.

579 citations


Cites background from "Intelligent distributed surveillanc..."

  • ...A review of general surveillance systems is provided in [79] and [139], with a particular focus on distributed surveillance systems....

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Book ChapterDOI
08 Oct 2016
TL;DR: This paper proposes a novel deep learning-based approach to PROgressive Vehicle re-ID, called “PROVID”, which treats vehicle Re-Id as two specific progressive search processes: coarse-to-fine search in the feature space, and near- to-distantsearch in the real world surveillance environment.
Abstract: While re-identification (Re-Id) of persons has attracted intensive attention, vehicle, which is a significant object class in urban video surveillance, is often overlooked by vision community. Most existing methods for vehicle Re-Id only achieve limited performance, as they predominantly focus on the generic appearance of vehicle while neglecting some unique identities of vehicle (e.g., license plate). In this paper, we propose a novel deep learning-based approach to PROgressive Vehicle re-ID, called “PROVID”. Our approach treats vehicle Re-Id as two specific progressive search processes: coarse-to-fine search in the feature space, and near-to-distant search in the real world surveillance environment. The first search process employs the appearance attributes of vehicle for a coarse filtering, and then exploits the Siamese Neural Network for license plate verification to accurately identify vehicles. The near-to-distant search process retrieves vehicles in a manner like human beings, by searching from near to faraway cameras and from close to distant time. Moreover, to facilitate progressive vehicle Re-Id research, we collect to-date the largest dataset named VeRi-776 from large-scale urban surveillance videos, which contains not only massive vehicles with diverse attributes and high recurrence rate, but also sufficient license plates and spatiotemporal labels. A comprehensive evaluation on the VeRi-776 shows that our approach outperforms the state-of-the-art methods by 9.28 % improvements in term of mAP.

450 citations


Cites background from "Intelligent distributed surveillanc..."

  • ...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....

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Journal ArticleDOI
01 Nov 2012
TL;DR: The main purpose of this survey is to extensively identify existing methods and characterize the literature in a manner that brings key challenges to attention.
Abstract: Modeling human behaviors and activity patterns for recognition or detection of special event has attracted significant research interest in recent years. Diverse methods that are abound for building intelligent vision systems aimed at scene understanding and making correct semantic inference from the observed dynamics of moving targets. Most applications are in surveillance, video content retrieval, and human-computer interfaces. This paper presents not only an update extending previous related surveys, but also a focus on contextual abnormal human behavior detection especially in video surveillance applications. The main purpose of this survey is to extensively identify existing methods and characterize the literature in a manner that brings key challenges to attention.

440 citations

Journal ArticleDOI
TL;DR: This paper provides an overview of benchmark databases for activity recognition, the market analysis of video surveillance, and future directions to work on for this application.
Abstract: This paper provides a comprehensive survey for activity recognition in video surveillance. It starts with a description of simple and complex human activity, and various applications. The applications of activity recognition are manifold, ranging from visual surveillance through content based retrieval to human computer interaction. The organization of this paper covers all aspects of the general framework of human activity recognition. Then it summarizes and categorizes recent-published research progresses under a general framework. Finally, this paper also provides an overview of benchmark databases for activity recognition, the market analysis of video surveillance, and future directions to work on for this application.

378 citations


Additional excerpts

  • ...Valera 05 Intelligent distributed surveillance [187]...

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References
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Journal ArticleDOI
TL;DR: Both optimal and suboptimal Bayesian algorithms for nonlinear/non-Gaussian tracking problems, with a focus on particle filters are reviewed.
Abstract: Increasingly, for many application areas, it is becoming important to include elements of nonlinearity and non-Gaussianity in order to model accurately the underlying dynamics of a physical system. Moreover, it is typically crucial to process data on-line as it arrives, both from the point of view of storage costs as well as for rapid adaptation to changing signal characteristics. In this paper, we review both optimal and suboptimal Bayesian algorithms for nonlinear/non-Gaussian tracking problems, with a focus on particle filters. Particle filters are sequential Monte Carlo methods based on point mass (or "particle") representations of probability densities, which can be applied to any state-space model and which generalize the traditional Kalman filtering methods. Several variants of the particle filter such as SIR, ASIR, and RPF are introduced within a generic framework of the sequential importance sampling (SIS) algorithm. These are discussed and compared with the standard EKF through an illustrative example.

11,409 citations

Journal ArticleDOI
TL;DR: Pfinder is a real-time system for tracking people and interpreting their behavior that uses a multiclass statistical model of color and shape to obtain a 2D representation of head and hands in a wide range of viewing conditions.
Abstract: Pfinder is a real-time system for tracking people and interpreting their behavior. It runs at 10 Hz on a standard SGI Indy computer, and has performed reliably on thousands of people in many different physical locations. The system uses a multiclass statistical model of color and shape to obtain a 2D representation of head and hands in a wide range of viewing conditions. Pfinder has been successfully used in a wide range of applications including wireless interfaces, video databases, and low-bandwidth coding.

4,280 citations

Journal ArticleDOI
TL;DR: This paper focuses on motion tracking and shows how one can use observed motion to learn patterns of activity in a site and create a hierarchical binary-tree classification of the representations within a sequence.
Abstract: Our goal is to develop a visual monitoring system that passively observes moving objects in a site and learns patterns of activity from those observations. For extended sites, the system will require multiple cameras. Thus, key elements of the system are motion tracking, camera coordination, activity classification, and event detection. In this paper, we focus on motion tracking and show how one can use observed motion to learn patterns of activity in a site. Motion segmentation is based on an adaptive background subtraction method that models each pixel as a mixture of Gaussians and uses an online approximation to update the model. The Gaussian distributions are then evaluated to determine which are most likely to result from a background process. This yields a stable, real-time outdoor tracker that reliably deals with lighting changes, repetitive motions from clutter, and long-term scene changes. While a tracking system is unaware of the identity of any object it tracks, the identity remains the same for the entire tracking sequence. Our system leverages this information by accumulating joint co-occurrences of the representations within a sequence. These joint co-occurrence statistics are then used to create a hierarchical binary-tree classification of the representations. This method is useful for classifying sequences, as well as individual instances of activities in a site.

3,631 citations

Journal ArticleDOI
TL;DR: W/sup 4/ employs a combination of shape analysis and tracking to locate people and their parts and to create models of people's appearance so that they can be tracked through interactions such as occlusions.
Abstract: W/sup 4/ is a real time visual surveillance system for detecting and tracking multiple people and monitoring their activities in an outdoor environment. It operates on monocular gray-scale video imagery, or on video imagery from an infrared camera. W/sup 4/ employs a combination of shape analysis and tracking to locate people and their parts (head, hands, feet, torso) and to create models of people's appearance so that they can be tracked through interactions such as occlusions. It can determine whether a foreground region contains multiple people and can segment the region into its constituent people and track them. W/sup 4/ can also determine whether people are carrying objects, and can segment objects from their silhouettes, and construct appearance models for them so they can be identified in subsequent frames. W/sup 4/ can recognize events between people and objects, such as depositing an object, exchanging bags, or removing an object. It runs at 25 Hz for 320/spl times/240 resolution images on a 400 MHz dual-Pentium II PC.

2,870 citations

Journal ArticleDOI
Dariu M. Gavrila1
TL;DR: A number of promising applications are identified and an overview of recent developments in this domain is provided, including work on whole-body or hand motion and the various methodologies.

2,045 citations