Urban environments present unique challenges from the perspective of surveillance and security. Threat activity in urban environments tends to be very similar to background activity, while the volume of activity is often very high. The widespread geographical area presents issues from the perspective of response. These characteristics of urban environments create challenges to traditional applications of video analytics technologies and opens up opportunities for novel approaches. This paper explores the applicability of video analytics in various scenarios presented in urban surveillance situations. We also describe novel technical solutions to some of the challenges of urban surveillance.

Surveillance video is used in two key modes, watching for known threats in real-time and searching for events of interest after the fact. Typically, real-time alerting is a localized function, e.g. airport security center receives and reacts to a “perimeter breach alert”, while investigations often tend to encompass a large number of geographically distributed cameras like the London bombing, or Washington sniper incidents. Enabling effective search of surveillance video for investigation & preemption, involves indexing the video along multiple dimensions. This paper presents a framework for surveillance search which includes, video parsing, indexing and query mechanisms. It explores video parsing techniques which automatically extract index data from video, indexing which stores data in relational tables, retrieval which uses SQL queries to retrieve events of interest and the software architecture that integrates these technologies.

Pervasive sensor based systems are transforming Information Technology systems from being transactional in nature to being observational in nature. Observational systems are inherently distributed and capture information at a much finer grain of space and time. Enabling and building such systems also poses many technology challenges, extracting information from sensor signals, indexing and searching sensor meta-data, data mining and scalability. In this paper we use S3: the IBM smart surveillance system as an example of an observational system to explore several of these issues through real world deployment examples.

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.

We present a new method to robustly and efficiently analyze foreground when we detect background for a fixed camera view by using mixture of Gaussians models and multiple cues. The background is modeled by three Gaussian mixtures as in the work of Stauffer and Grimson (1999). Then the intensity and texture information are integrated to remove shadows and to enable the algorithm working for quick lighting changes. For foreground analysis, the same Gaussian mixture model is employed to detect the static foreground regions without using any tracking or motion information. Then the whole static regions are pushed back to the background model to avoid a common problem in background subtraction /spl times/ fragmentation (one object becomes multiple parts). The method was tested on our real time video surveillance system. It is robust and run about 130 fpsfor color images and 150 fps for grayscale images at size 160/spl times/120 on a 2GB Pentium IV machine with MMX optimization.