Advances in deep neural networks (DNN) and computer vision (CV) algorithms have made it feasible to extract meaningful insights from large-scale deployments of urban cameras. Tracking an object of interest across the camera network in near real-time is a canonical problem. However, current tracking frameworks have two key limitations: 1) They are monolithic, proprietary, and lack the ability to rapidly incorporate sophisticated tracking models; and 2) They are less responsive to dynamism across wide-area computing resources that include edge, fog and cloud abstractions. We address these gaps using Anveshak, a runtime platform for composing and coordinating distributed tracking applications. It provides a domain-specific dataflow programming model to intuitively compose a tracking application, supporting contemporary CV advances like query fusion and re-identification, and enabling dynamic scoping of the camera-network's search space to avoid wasted computation. We also offer tunable batching and data-dropping strategies for dataflow blocks deployed on distributed resources to respond to network and compute variability. These balance the tracking accuracy, its real-time performance and the active camera-set size. We illustrate the concise expressiveness of the programming model for 4 tracking applications. Our detailed experiments for a network of 1000 camera-feeds on modest resources exhibit the tunable scalability, performance and quality trade-offs enabled by our dynamic tracking, batching and dropping strategies.
 ADVISOR is an automated visual surveillance system for metro stations which was developed as part of the project ADVISOR, involving 3 academic and 3 industrial project partners. The ADVISOR system aims at making public transport safer by automatically detecting at an early stage dangerous situations which may lead to accidents, violence or vandalism. In order to achieve this people are tracked across the station and their behaviours analysed. Additional measurements on crowd density and movement are also obtained. Warnings are generated and displayed to human operators for possible intervention. The article explores the main difficulties encountered during the design and implementation of ADVISOR and describes the ways in which they were solved. A prototype system has been built and extensively tested, proving the feasibility of automated visual surveillance systems. An analysis of test runs at a metro station in Barcelona and several individual experiments show that the system copes with many difficult image analysis problems. The analysis also points the way for future development and ways of deployment of the techniques used in the system. As smart surveillance technology becomes a critical component in security infrastructures, the system architecture assumes a critical importance. This paper considers the example of smart surveillance in an airport environment. We start with a threat model for airports and use this to derive the security requirements. These requirements are used to motivate an open-standards based architecture for surveillance. We discuss the critical aspects of this architecture and its implementation in the IBM S3 smart surveillance system. Demo results from a pilot deployment in Hawthorne, NY are presented. Graduates of computer science (CS) and software engineering (SE) programs are typically employed to develop industry-strength software. Computer engineering (CE) programs focus primarily on computing-system design, often with significant software components. These three programs have different emphases: development of new algorithms versus development of large, complex software systems versus development of small embedded software and device drivers. All three areas require good SE practices. The use of multiple heterogeneous cameras is becoming more common in today's surveillance systems. In order to perform surveillance tasks, effective coordination and control in multi-camera systems is very important, and is catching significant research attention these days. This survey aims to provide researchers with a state-of-the-art overview of various techniques for multi-camera coordination and control (MC3) that have been adopted in surveillance systems. The existing literature on MC3 is presented through several classifications based on the applicable architectures, frameworks and the associated surveillance tasks. Finally, a discussion on the open problems in surveillance area that can be solved effectively using MC3 and the future directions in MC3 research is presented