The Design and Implementation of a Wireless Video Surveillance System

Zhang, Tan; Chowdhery, Aakanksha; Bahl, Paramvir; Jamieson, Kyle; Banerjee, Suman

doi:10.1145/2789168.2790123

Cited by 226 publications

(134 citation statements)

References 30 publications

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“…The active cache technique can be applied to any vision application that has real-time constraints. For example, a surveillance control system [61] or video analysis application that tracks objects such as cars needs to hide the recognition delay to track the location of objects in real time. The trigger frame technique can be applied to any vision processing system to reduce the resources consumed.…”

Section: Discussionmentioning

confidence: 99%

Glimpse

Chen

Ravindranath

Deng

et al. 2015

Proceedings of the 13th ACM Conference on Embedded Networked Sensor Systems

279

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Glimpse is a continuous, real-time object recognition system for camera-equipped mobile devices. Glimpse captures full-motion video, locates objects of interest, recognizes and labels them, and tracks them from frame to frame for the user. Because the algorithms for object recognition entail significant computation, Glimpse runs them on server machines. When the latency between the server and mobile device is higher than a frame-time, this approach lowers objectrecognition accuracy. To regain accuracy, Glimpse uses an active cache of video frames on the mobile device. A subset of the frames in the active cache are used to track objects on the mobile, using (stale) hints about objects that arrive from the server from time to time. To reduce network bandwidth usage, Glimpse computes trigger frames to send to the server for recognizing and labeling. Experiments with Android smartphones and Google Glass over Verizon, AT&T, and a campus Wi-Fi network show that with hardware face detection support (available on many mobile devices), Glimpse achieves precision between 96.4% to 99.8% for continuous face recognition, which improves over a scheme performing hardware face detection and server-side recognition without Glimpse's techniques by between 1.8-2.5×. The improvement in precision for face recognition without hardware detection is between 1.6-5.5×. For road sign recognition, which does not have a hardware detector, Glimpse achieves precision between 75% and 80%; without Glimpse, continuous detection is non-functional (0.2%-1.9% precision). Permission to make digital or hard copies of all or part of this work for personal or classroom use is granted without fee provided that copies are not made or distributed for profit or commercial advantage and that copies bear this notice and the full citation on the first page. Copyrights for components of this work owned by others than ACM must be honored. Abstracting with credit is permitted. To copy otherwise, or republish, to post on servers or to redistribute to lists, requires prior specific permission and/or a fee. Request permissions from Permissions@acm.org. Categories and Subject Descriptors

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Section: Discussionmentioning

confidence: 99%

Glimpse

Chen

Ravindranath

Deng

et al. 2015

Proceedings of the 13th ACM Conference on Embedded Networked Sensor Systems

279

View full text Add to dashboard Cite

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“…Zhag et al [26] apply the data importance concept to eliminate redundant observations made by multiple surveillance cameras. In [27], visual data in a squash game is analyzed.…”

Section: Data Importancementioning

confidence: 99%

“…Although notion of data importance has been considered in various contexts [2,[25][26][27][28]30], our work is different from them in that we (1) enhance the total utility via adaptive sensor data transfer from IoT devices to the edge server based on data importance and (2) provide a real-time sensor data analytics framework at the network edge, while supporting generic data importance metrics.…”

Section: Related Workmentioning

confidence: 99%

Real-Time Information Derivation from Big Sensor Data via Edge Computing

Kang

Chen

et al. 2017

BDCC

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Abstract:In data-intensive real-time applications, e.g., cognitive assistance and mobile health (mHealth), the amount of sensor data is exploding. In these applications, it is desirable to extract value-added information, e.g., mental or physical health conditions, from sensor data streams in real-time rather than overloading users with massive raw data. However, achieving the objective is challenging due to the data volume and complex data analysis tasks with stringent timing constraints. Most existing big data management systems, e.g., Hadoop, are not directly applicable to real-time sensor data analytics, since they are timing agnostic and focus on batch processing of previously stored data that are potentially outdated and subject to I/O overheads. Moreover, embedded sensors and IoT devices lack enough resources to perform sophisticated data analytics. To address the problem, we design a new real-time big data management framework to support periodic in-memory real-time sensor data analytics at the network edge by extending the map-reduce model originated in functional programming, while providing adaptive sensor data transfer to the edge server based on data importance. In this paper, a prototype system is designed and implemented as a proof of concept. In the performance evaluation, it is empirically shown that important sensor data are delivered in a preferred manner and they are analyzed in a timely fashion.

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“…Sensors detect intruders by infrared motion detectors (active and passive types), ultrasound motion detectors etc. The sensors may be directly wired to a control panel that provide sensor power, or may communicate wirelessly [3]. Using the active infrared as a sensor to detect intrusion has advantages of fast response, and is insensitive to mechanical and acoustics noise.…”

Section: Introductionmentioning

confidence: 99%

Analysis Of Physical Parameters Using Embedded Programmable Operating System

2018

IJRTER

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Abstract-In the last decades, monitoring cameras begin to play a vital role in securing sensitive systems such as government sites or establishments. Generally, these kinds of cameras are fixed-location (i.e. outdoor camera) such that the viewpoint is limited to small area and not covering the whole place. In addition, there are some drawbacks that appear when using these kinds of cameras such as being breakable (intentionally or not) which may lead to camera malfunction or breaking in the linked electrical wires that may cause disconnection between the camera, monitor and its receiver. However, the main problem is the lacking of secure protecting system that prevents intruders from entering into the system disabling or malfunction it.In this research a new system is proposed in order to solve these problems by using wireless-mobile camera with embedded programmable operating system which enables controlling this camera remotely by sending wireless commands through the embedded component called Arduino card controller. This card enables the connection between the camera and the server to be programmatic by the user or developer. The main goal of this research is to design a monitoring system to detect any suspicious events and to ensure that the transferring monitoring data from the camera to the server is not infiltrated by unauthorized person by applying a set of techniques from image detection, object tracking and security algorithms to the instructions or the program of the camera. Compared with other researches, this work achieved the following goals: 1-Using Arduino card for programming the camera. 2-IP camera does not require user name and password. 3-The images and the other information are (encrypted) when sending to/from computer, 4-Using Mobile-wireless camera. 5-Process of keys exchanging between camera and computer. The results of this research are good and achieved the main goals of new developed technique.

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The Design and Implementation of a Wireless Video Surveillance System

Cited by 226 publications

References 30 publications

Glimpse

Glimpse

Real-Time Information Derivation from Big Sensor Data via Edge Computing

Analysis Of Physical Parameters Using Embedded Programmable Operating System

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