Mobile Video Surveillance Systems: An Architectural Overview

Cucchiara, Rita; Gualdi, Giovanni

doi:10.1007/978-3-642-12349-8_6

Cited by 17 publications

(11 citation statements)

References 37 publications

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“…Cucchiara and Gauldi [7] categorized the mobility of surveillance cameras into three types, i.e., (i) fixed cameras, (ii) mobile cameras that can be deployed anywhere but do not move while operating, and (iii) mobile cameras that freely move even while operating. Our proposed system can be categorized as (iii).…”

Section: Application Scenario and Goalmentioning

confidence: 99%

“…With the recent popularization of mobile video cameras including camera phones, mobile video surveillance, which uses mobile video cameras for surveillance, draws much attention [2,4,7,22,37]. For example, New York City's emergency system accepts videos of an unusual event such as crimes and disaster that are taken by ordinary people with mobile video cameras, and Manchester police department implemented a similar system as introduced in [37].…”

Section: Introductionmentioning

confidence: 99%

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Intended human object detection for automatically protecting privacy in mobile video surveillance

2011

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With the recent popularization of mobile video cameras including camera phones, a new technology, mobile video surveillance, which uses mobile video cameras for video surveillance has been emerging. Such videos, however, may infringe upon the privacy of others by disclosing privacy sensitive information (PSI), i.e., their appearances. To prevent videos from infringing on the right to privacy, new techniques are required that automatically obscure PSI regions. The problem is how to determine the PSI regions to be obscured while maintaining enough video content to present the camera persons' capture-intentions, i.e., what they want to record in their videos to achieve their surveillance tasks. To this end, we introduce a new concept called intended human objects that are defined as human objects essential for capture-intentions, and develop a new method called intended human object detection that automatically detects the intended human objects in videos taken by different camera persons. Through the process of intended human object detection, we develop a system for automatically obscuring PSI regions. We experimentally show the performance of intended human object detection and the contributions of the features used. Our user study shows the potential applicability of our proposed system.

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Section: Application Scenario and Goalmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Intended human object detection for automatically protecting privacy in mobile video surveillance

2011

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“…Mobile video monitoring system is a combination of computer, the video transmitter, mobile network and video coding technology. It can transmit on-site real-time information of different locations through mobile network to the monitoring center or handheld devices [7]. With mobile communication transmission into the 3G era, bandwidth of mobile data transmission is increased sharply, as solved the transmission bandwidth bottlenecks of video monitoring business on the mobile network.…”

Section: B Mobile Video Monitoringmentioning

confidence: 99%

Design and implementation of real-time image monitoring system based on Android mobile terminal

Wang

Fan

2014

2014 IEEE 7th Joint International Information Technology and Artificial Intelligence Conference

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This paper designs an Android-based mobile terminal real-time image monitoring system, making the remote monitoring of Android-based mobile terminal through Short Message Service (SMS) come into being, and thus gaining the image of Remote Mobile Terminals (RMT). The overall function of the process is: install this system on an Android-based RMT; register monitoring number and monitoring code; when the Local Monitor Terminal (LMT) sends SMS commands to RMT, SMS monitoring module of this system captures SMS, separates the incoming number and content and compares them respectively with the registered records in the SQLite database. If the match is successful, a message is replied and the LMT sends the information code needed to acquire according to the content of the message. The RMT takes photos and replies Multimedia Messaging Service (MMS) according to the code. This system can be operated normally after testing, and shows promising application value.

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“…For mobile surveillance, which captures suspicious people using mobile cameras, some privacy-aware systems are summarized in [43], e.g., [44], [45], and a more recent work focused on automatically discriminating people of interests based on the videographer's intention [46]. Besides mobile video surveillance, privacy protection has been designed for various applications.…”

Section: Related Workmentioning

confidence: 99%

Protection and Utilization of Privacy Information via Sensing

Babaguchi

Nakashima

2015

IEICE Trans. Inf. ^|^ Syst.

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SUMMARYOur society has been getting more privacy-sensitive. Diverse information is given by users to information and communications technology (ICT) systems such as IC cards benefiting them. The information is stored as so-called big data, and there is concern over privacy violation. Visual information such as images and videos is also considered privacy-sensitive. The growing deployment of surveillance cameras and social network services has caused a privacy problem of information given from various sensors. To protect privacy of subjects presented in visual information, their face or figure is processed by means of pixelization or blurring. As image analysis technologies have made considerable progress, many attempts to automatically process flexible privacy protection have been made since 2000, and utilization of privacy information under some restrictions has been taken into account in recent years. This paper addresses the recent progress of privacy protection for visual information, showing our research projects: PriSurv, Digital Diorama (DD), and Mobile Privacy Protection (MPP). Furthermore, we discuss Harmonized Information Field (HIFI) for appropriate utilization of protected privacy information in a specific area.

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Mobile Video Surveillance Systems: An Architectural Overview

Cited by 17 publications

References 37 publications

Intended human object detection for automatically protecting privacy in mobile video surveillance

Intended human object detection for automatically protecting privacy in mobile video surveillance

Design and implementation of real-time image monitoring system based on Android mobile terminal

Protection and Utilization of Privacy Information via Sensing

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