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.

/pdf/intelligent-distributed-surveillance-systems-a-review-1jp146ld4r.pdf

Intelligent distributed surveillance systems: a review

https://www.ee.cuhk.edu.hk/~xgwang/papers/multicam_review.pdf

Intelligent multi-camera video surveillance: A review

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.

A Review of Computer Vision Techniques for the Analysis of Urban Traffic

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.

/pdf/a-deep-learning-based-approach-to-progressive-vehicle-re-5ebvwg9l1z.pdf

A Deep Learning-Based Approach to Progressive Vehicle Re-identification for Urban Surveillance

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.

Video-Based Abnormal Human Behavior Recognition—A Review

A review of the state-of-the-art in distributed surveillance systems

A new generation of advanced surveillance systems is being conceived as a collection of multisensor components such as video, audio, and mobile robots interacting in a cooperating manner to enhance situation awareness capabilities to assist surveillance personnel. The prominent issues that these systems face are the improvement of existing intelligent video surveillance systems, the inclusion of wireless networks, the use of low power sensors, the design architecture, the communication between different components, the fusion of data emerging from different type of sensors, the location of personnel (providers and consumers), and the scalability of the system. This paper focuses on the aspects pertaining to real-time distributed architecture and scalability. For example, to meet real-time requirements, these systems need to process data streams in concurrent environments, designed by taking into account scheduling and synchronization. This paper proposes a framework for the design of visual surveillance systems based on components derived from the principles of real-time networks/data-oriented requirements implementation scheme. It also proposes the implementation of these components using the well-known middleware technology common object request broker architecture. Results using this architecture for video surveillance are presented through an implemented prototype.

Communication Mechanisms and Middleware for Distributed Video Surveillance

The main aim of this PhD is to investigate how a methodology rooted in systems engineering concepts can be established and applied to the design of distributed wide-area visual surveillance systems. Nowadays, the research community in surveillance systems tends to be mostly focused on the computer vision part of these systems, researching and developing more intelligent algorithms. The integration and finally the creation of the system per se, are usually regarded as a secondary priority. We postulate here that until a robust systems-centred, rather than algorithmic-centred approach is used, the realisation of realistic distributed surveillance systems is unlikely to happen.

The future generation of surveillance systems can be categorised, from a system engineering point of view, as concurrent, distributed, embedded, real time systems. An important aspect of these systems is the inherent temporal diversity (heterogeneous timing) that arises from a variety of timing requirements and from the parallelisation and distribution of the processes that compose the system. Embedded, real-time systems are often naturally asynchronous. However, the computer vision part of these surveillance systems is commonly conceived and designed in a sequential and synchronous manner, in many cases using an object-oriented approach. Moreover, to cope with the distributed nature of these systems, technologies such as CORBA are applied. Designing processes in a synchronous manner plus the run-time overheads associated with object oriented implementations may cause communication bottlenecks. Perhaps more importantly, it may produce unpredictable behaviour of some components of the system and hence undetermined performance from a system as a whole. Clearly, this is a major problem on surveillance systems that can often be expected to be safety-critical.

This research has explored the use of an alternative approach to object-orientation for the design and implementation of intelligent distributed surveillance systems. The approach is known as Real-Time Networks (exemplified by system engineering methodologies such as MASCOT and extensions such as DORIS). This approach is based conceptually on conceiving solutions as being naturally concurrent, from the highest level of abstraction, with concurrent activities communicating through well-defined data-centred mechanisms. The methodology favours a disciplined approach to design, which yields a modular structure that has close correspondence between functional elements in design and constructional elements for system integration. It is such characteristics that we believe will become essential in overcoming the complexities of going from small-scale computer vision prototypes to large-scale working systems.
 
To justify the selection of this methodology, an overview of different software approach methods that may be used for designing wide-area intelligent surveillance systems is given. This is then, narrowed down to a comparison between Real-Time Networks and Object Orientation. The comparison is followed by an illustration of two different design solutions of an existing real-time distributed surveillance system called ADVISOR. One of the design solutions, based on Object Oriented concepts, uses CORBA as a means for the integration and distribution characteristics of the system. The other design solution, based on Real-Time Networks, uses DORIS methodology as a solution for the design of the system. Once the justification over the selection is done, a' novel design of a generic visual surveillance system using the proposed Real-Time Networks method is presented. Finally, the conclusions and future work are explained in the last chapter.

An approach for designing a real-time intelligent distributed surveillance system

The latest generation of surveillance systems can be categorised as concurrent, distributed and large real-time systems. The most common and well-known approach to design these systems is based on object oriented technology. We present another approach to design an intelligent distributed surveillance system. The approach method is known as "real time network approach" or MASCOT, a design method for designing and implementing large real-time concurrent systems. The basic notion is that the flow of data through the system is controlled solely by a set of concurrent processes. We introduce the fundamental concepts of MASCOT and its possible contributions to the creation of good designs for such intelligent distributed surveillance systems. We finish with the illustration of a distributed real time surveillance system using this approach.

M. Valera

Papers

Intelligent distributed surveillance systems: a review

A review of the state-of-the-art in distributed surveillance systems

Communication Mechanisms and Middleware for Distributed Video Surveillance

An approach for designing a real-time intelligent distributed surveillance system

Real-time architecture for a large distributed surveillance system