Event Detection, Query, and Retrieval for Video Surveillance

Event Detection, Query, and Retrieval for Video Surveillance

Ying-li Tian (IBM T. J. Watson Research Center, USA), Arun Hampapur (IBM T. J. Watson Research Center, USA), Lisa Brown (IBM T. J. Watson Research Center, USA), Rogerio Feris (IBM T. J. Watson Research Center, USA), Max Lu (IBM T. J. Watson Research Center, USA) and Andrew Senior (IBM T. J. Watson Research Center, USA)
DOI: 10.4018/978-1-60566-174-2.ch015
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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, for example, 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.
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2. Background

Video surveillance systems which run 24/7 (24 hours a day and seven days a week) create a large amount of data including videos, extracted features, alerts, statistics etc. Designing systems to manage this extensive data and make it easily accessible for query and search is a very challenging and potentially rewarding problem. However, the vast majority of research in video indexing has taken place in the field of multimedia, in particular for authored or produced video such as news or movies, and spontaneous but broadcast video such as sporting events. Efforts to apply video indexing to completely spontaneous video such as surveillance data are still emerging.

The work in video indexing of broadcast video has focused on such tasks as shot boundary detection, story segmentation and high level semantic concept extraction. The latter is based on the classification of video, audio, and text into a small (10-20) but increasing number of semantically interesting categories such as outdoor, people, building, road, vegetation, and vehicle. For broadcast video, the goal is to find a high level indexing scheme to facilitate retrieval. The task objectives are very different for surveillance video. For surveillance video, the primary interest is to learn higher level behavior patterns. In both broadcast and surveillance video, there exists a semantic gap between the feasible low level feature set and the high level semantics or ontology desired by the system users.

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Table of Contents
Zongmin Ma
Chapter 1
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Chapter 2
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Texture feature extraction and description is one of the important research contents in content-based medical image retrieval. The chapter first... Sample PDF
Review on Texture Feature Extraction and Description Methods in Content-Based Medical Image Retrieval
Chapter 4
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Advances in data storage and image acquisition technologies have enabled the creation of large image datasets. Thus, it is necessary to develop... Sample PDF
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Chapter 5
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Content-based image retrieval has been an active research area in past years. Many different solutions have been proposed to improve performance of... Sample PDF
Content Based Image Retrieval Using Active-Nets
Chapter 6
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Content-Based Image Retrieval (CBIR) aims to search images that are perceptually similar to the querybased on visual content of the images without... Sample PDF
Content-Based Image Retrieval: From the Object Detection/Recognition Point of View
Chapter 7
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After the generation of multimedia data turning digital, an explosion of interest in their data storage, retrieval, and processing, has drastically... Sample PDF
Making Image Retrieval and Classification More Accurate Using Time Series and Learned Constraints
Chapter 8
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A multimedia index makes it possible to group data according to similarity criteria. Traditional index structures are based on trees and use the... Sample PDF
A Machine Learning-Based Model for Content-Based Image Retrieval
Chapter 9
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This chapter studies the user relevance feedback in image retrieval. We take this problem as a standard two-class pattern classification problem... Sample PDF
Solving the Small and Asymmetric Sampling Problem in the Context of Image Retrieval
Chapter 10
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An image is a symbolic representation; people interpret an image and associate semantics with it based on their subjective perceptions, which... Sample PDF
Content Analysis from User's Relevance Feedback for Content-Based Image Retrieval
Chapter 11
Pawel Rotter, Andrzej M.J. Skulimowski
In this chapter, we describe two new approaches to content-based image retrieval (CBIR) based on preference information provided by the user... Sample PDF
Preference Extraction in Image Retrieval
Chapter 12
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Personalized Content-Based Image Retrieval
Chapter 13
Zhiping Shi, Qingyong Li, Qing He, Zhongzhi Shi
Semantics-based retrieval is a trend of the Content-Based Multimedia Retrieval (CBMR). Typically, in multimedia databases, there exist two kinds of... Sample PDF
A Semantics Sensitive Framework of Organization and Retrieval for Multimedia Databases
Chapter 14
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Content-Based Retrieval for Mammograms
Chapter 15
Ying-li Tian, Arun Hampapur, Lisa Brown, Rogerio Feris, Max Lu, Andrew Senior
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Event Detection, Query, and Retrieval for Video Surveillance
Chapter 16
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MMIR: An Advanced Content-Based Image Retrieval System Using a Hierarchical Learning Framework
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