Current Issue : July - September Volume : 2018 Issue Number : 3 Articles : 5 Articles
Wireless multimedia sensor networks (WMSNs) are increasingly being deployed for\nsurveillance and monitoring applications. WMSNs applications produce large amount of data, which\nrequire high transmission rates. An efficient and seamless delivery of multimedia services in WMSNs\nis still a challenging task. This article proposes an intelligent video surveillance platform (IVSP) for\nwireless multimedia sensor networks. IVSP presents the design of a networked system for joint rate\ncontrol and error control of video over resource-constrained embedded devices. First, a combination\nof two different congestion indicators is introduced to differentiate between congestion levels and\nhandle them accordingly. Second, a feedback-based rate controller is developed to maximize received\nvideo quality, in which sensor nodes can adaptively adjust their sending rates. Finally, a different\nretransmission mechanism for different packets is proposed. Lost packets can be stored temporarily\nand resend when free channel is available to avoid congestion. The core component of IVSP is an\nopen source hardware platform, which is based on Raspberry Pi sensor nodes. IVSP is extensively\nevaluated on 7 Raspberry Pi sensor nodes. We present the results of 7-node real-world deployment\nof IVSP in a video surveillance application and show that it works well in long-term deployments....
With the rise in biometric-based identity authentication, facial recognition software has already stimulated interesting research.\nHowever, facial recognition has also been subjected to criticism due to security concerns.The main attack methods include photo,\nvideo, and three-dimensional model attacks. In this paper, we propose a multifeature fusion scheme that combines dynamic and\nstatic joint analysis to detect fake face attacks. Since the texture differences between the real and the fake faces can be easily detected,\nLBP (local binary patter) texture operators and optical flow algorithms are often merged. Basic LBP methods are also modified by\nconsidering the nearest neighbour binary computing method instead of the fixed centre pixel method; the traditional optical flow\nalgorithm is also modified by applying the multifusion feature superposition method, which reduces the noise of the image. In\nthe pyramid model, image processing is performed in each layer by using block calculations that form multiple block images.\nThe features of the image are obtained via two fused algorithms (MOLF), which are then trained and tested separately by an\nSVM classifier. Experimental results show that this method can improve detection accuracy while also reducing computational\ncomplexity. In this paper, we use the CASIA, PRINT-ATTACK, and REPLAY-ATTACK database to compare the various LBP\nalgorithms that incorporate optical flow and fusion algorithms...
Quality of Experience (QoE) of video streaming services has been attracting more and more attention recently. Therefore, in this\nwork we designed and implemented a real-time QoE monitoring system for streaming services with Adaptive Media Playout\n(AMP), which was implemented into the VideoLAN Client (VLC) media player to dynamically adjust the playout rate of videos\naccording to the buffer fullness of the client buffer. The QoE monitoring system reports the QoE of streaming services in real\ntime so that network/content providers can monitor the qualities of their services and resolve troubles immediately whenever their\nsubscribers encounter them. Several experiments including wired and wireless streaming were conducted to show the effectiveness\nof the implemented AMP and QoE monitoring system. Experimental results demonstrate that AMP significantly improves the\nQoE of streaming services according to the Mean Opinion Score (MOS) estimated by our developed program. Additionally, some\nchallenging issues in wireless streaming have been easily identified using the developed QoE monitoring system....
A wide range of multimedia services is expected to be offered for mobile users via various\nwireless access networks. Even the integration of Cloud Computing in such networks does not\nsupport an adequate Quality of Experience (QoE) in areas with high demands for multimedia\ncontents. Fog computing has been conceptualized to facilitate the deployment of new services that\ncloud computing cannot provide, particularly those demanding QoE guarantees. These services\nare provided using fog nodes located at the network edge, which is capable of virtualizing their\nfunctions/applications. Service migration from the cloud to fog nodes can be actuated by request\npatterns and the timing issues. To the best of our knowledge, existing works on fog computing focus\non architecture and fog node deployment issues. In this article, we describe the operational impacts\nand benefits associated with service migration from the cloud to multi-tier fog computing for video\ndistribution with QoE support. Besides that, we perform the evaluation of such service migration of\nvideo services. Finally, we present potential research challenges and trends...
Shape matching plays an important role\nin various computer vision and graphics applications\nsuch as shape retrieval, object detection, image editing,\nimage retrieval, etc. However, detecting shapes in\ncluttered images is still quite challenging due to the\nincomplete edges and changing perspective. In this\npaper, we propose a novel approach that can efficiently\nidentify a queried shape in a cluttered image. The core\nidea is to acquire the transformation from the queried\nshape to the cluttered image by summarising all pointto-\npoint transformations between the queried shape\nand the image. To do so, we adopt a point-based shape\ndescriptor, the pyramid of arc-length descriptor (PAD),\nto identify point pairs between the queried shape and\nthe image having similar local shapes. We further\ncalculate the transformations between the identified\npoint pairs based on PAD. Finally, we summarise\nall transformations in a 4D transformation histogram\nand search for the main cluster. Our method can\nhandle both closed shapes and open curves, and is\nresistant to partial occlusions. Experiments show that\nour method can robustly detect shapes in images in\nthe presence of partial occlusions, fragile edges, and\ncluttered backgrounds....
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