Book chapter for Artificial Intelligence for Maximizing Content Based Image Retrieval Event detection, query, and retrieval for video surveillance

Video surveillance automation 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. an 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 event detection, query and retrieval of surveillance video for preemption and investigation involves indexing the video along multiple dimensions. This chapter presents a framework for event detection and surveillance search that includes: video parsing, indexing, query and retrieval mechanisms. It explores video parsing techniques that 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.