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This article addresses the usage and scope of Big Data Analytics in video surveillance and its potential application areas. The current age of technology provides the users, ample opportunity to generate data at every instant of t...
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This article addresses the usage and scope of Big Data Analytics in video surveillance and its potential application areas. The current age of technology provides the users, ample opportunity to generate data at every instant of time. Thus in general, a tremendous amount of data is generated every instant throughout the world. Among them, amount of video data generated is having a major share. Education, healthcare, tours and travels, food and culture, geographical exploration, agriculture, safety and security, entertainment etc., are the key areas where a tremendous amount of video data is generated every day. A major share among it are taken by the daily used surveillance data captured from the security purpose camera and are recorded everyday. Storage, retrieval, processing, and analysis of such gigantic data require some specific platform. Big Data Analytics is such a platform, which eases this analysis task. The aim of this article is to investigate the current trends in video surveillance and its applications using Big Data Analytics. It also aims to focus on the research opportunities for visual surveillance in Big Data frameworks. We have reported here the state-of-the-art surveillance schemes for four different imaging modalities: conventional video scene, remotely sensed video, medical diagnostics, and underwater surveillance. Several works were reported in this research field over recent years and are categorized based on the challenges solved by the researchers. A list of tools used for video surveillance using Big Data framework is presented. Finally, research gaps in this domain are discussed.
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In this article, a local change detection technique for underwater video sequences is proposed to detect the positions of the moving objects. The proposed change detection scheme integrates the Mixture of Gaussian (MoG) process in...
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In this article, a local change detection technique for underwater video sequences is proposed to detect the positions of the moving objects. The proposed change detection scheme integrates the Mixture of Gaussian (MoG) process in a Wronskian framework. It uses spatiotemporal modes (an integration of spatio-contextual and temporal modes) arising over the underwater video sequences to detect the local changes. The Wronskian framework takes care of the spatio-contextual modes whereas MoG models the temporal modes arising due to inter-dependency of a pixel in a video. The proposed scheme follows two steps: background construction and background subtraction. It takes initial few frames to construct a background model and thereby detection of the moving objects in the subsequent frames. During background construction stage; the linear dependency test between the region of supports/ local image patch in the target image frame and the reference background model are carried out using the Wronskian change detection model. The pixel values those are linearly dependent are assumed to be generated from an MoG process and are modeled using the same. Once the background is constructed, then the background subtraction and update process starts from the next frame. The efficiency of the proposed scheme is validated by testing it on two benchmark underwater video databases: fish4knowledge and underwater-changedetection and one large scale outdoor video database: changedetection.net. The effectiveness of the proposed scheme is demonstrated by comparing it with eighteen state-of-the-art local change detection algorithms. The performance of the proposed scheme is carried out using one subjective and three quantitative evaluation measures. (C) 2017 Elsevier Ltd. All rights reserved.
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Ultra wideband (UWB) is the most preferred candidate for body area networks (BAN). The higher data rate and lower multipath fading makes it highly suitable for the design of BAN. However, narrowband interference (NBI) may signific...
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Ultra wideband (UWB) is the most preferred candidate for body area networks (BAN). The higher data rate and lower multipath fading makes it highly suitable for the design of BAN. However, narrowband interference (NBI) may significantly degrade the performance of UWB. The paper presents an effective method of NBI mitigation for UWB BAN. The method uses modified Hermite pulse (MHP) in lieu of Gaussian and other pulse shapes. The spectral characteristics of the MHP make them immune to interference. The performance has been tested in various body postures in the CM4 channel model of the BAN, and further validated by transmitting medical signals like electrocardiography and MRI. The results show that MHP pulse is highly immune to NBI.
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Wireless body area networks (BAN) are primarily being designed for the detection of diseases and tracking of patients on the move. Due to its low-power requirements, ultra-wideband (UWB) has become the most preferred candidate for...
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Wireless body area networks (BAN) are primarily being designed for the detection of diseases and tracking of patients on the move. Due to its low-power requirements, ultra-wideband (UWB) has become the most preferred candidate for the physical layer of BAN. However, UWB suffers from the interferences from existing wireless networks. Again, the interference mitigation techniques developed for UWB will not be feasible for BAN because of their complexity and resulting power consumption. In this paper, we have proposed a hybrid Hermite pulse for cancelling multiple narrowband interferences. A comparison of the proposed technique with existing techniques is presented based on the operations count that highlights the lower complexity of the proposed technique. The proposed technique is also compared to Gaussian pulse based hybrid pulse in the simulation section to prove its superiority. Mathematical and simulation results prove that besides being of lower complexity the technique can mitigate the interferences satisfactorily.
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Body area networks (BANs) are wireless networks around the human body. Ultra-wideband (UWB) is one of the most preferred candidates for the physical layer of BAN. However, the attenuation due to human body tissues and the movement...
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Body area networks (BANs) are wireless networks around the human body. Ultra-wideband (UWB) is one of the most preferred candidates for the physical layer of BAN. However, the attenuation due to human body tissues and the movement of body parts with respect to each other causes severe attenuation and limits the range of UWB. Hence, new and novel methods are required to improve communications in UWB BAN. The paper proposes, hybrid amplify forward and decode forward relays for communication in body area networks. Scenarios in the presence of multiple relays for both body surface and body surface to external models have been taken into consideration. The performance has been tested using IEEE 802.15 task group 6 designated channel models developed from real-time measurements. The findings from the analytical as well as simulation-based study indicate that hybrid relaying provides a versatile performance, and improves the reliability of the communication link by decreasing the bit error rate. The performance has been further validated by the transmission of medical magnetic resonance images. The results indicate that hybrid relaying has the best performance, and it can undoubtedly improve the reliability of the body area networks.
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Body area networks (BAN) are a new variety of wireless sensor networks which are being developed to track patients' health. The 3.1-10.6 GHz ultra wideband band based BAN design has received huge commendation due to its capacity t...
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Body area networks (BAN) are a new variety of wireless sensor networks which are being developed to track patients' health. The 3.1-10.6 GHz ultra wideband band based BAN design has received huge commendation due to its capacity to provide high data rates and ability to carry medical signals of high bandwidth, on the sombre side however, its range is restricted in the human body channel. It requires novel techniques to improve the reliability and range. This paper proposes a novel approach to this problem. A solution using cooperative relaying has been proposed, that is not only unique, but also superior to classical cooperative relaying techniques. A cooperative relaying scheme employing hybrid relays, capable of a combination of 'Amplify and Forward' and 'Decode and Forward' relaying is used. In this method the signal to noise ratio, and mean square error, for electrocardiogram, blood pressure, electroencephalograms, and peak signal to noise ratio for magnetic resonance images, retinal and iris images, are measured at the relay and a decision is made to select relaying strategy. The work is unique in the aspect that, simulations have been performed and a comparative performance is presented, which suggests that SNR improvements of at least 3 dB, and quality of received medical signals is vastly enhanced.
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Ultra-wideband (UWB)-based body area networks (BAN) are envisioned to provide health care to patients on the move, and monitor patients' health. Since UWB uses transmit power lower than 1 mW, the link performance degrades due to a...
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Ultra-wideband (UWB)-based body area networks (BAN) are envisioned to provide health care to patients on the move, and monitor patients' health. Since UWB uses transmit power lower than 1 mW, the link performance degrades due to absorption in body tissues and channel impairments. In BAN, multiple sensor nodes present between the transmitter and the receiver can act as relays to improve the link quality, thereby improving the energy efficiency and reliability.
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Body area networks are being developed to serve a wide range of purposes ranging from providing health care to patients on the move to tracking patients and motion sensing for gaming controls. There has been significant and sizeab...
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Body area networks are being developed to serve a wide range of purposes ranging from providing health care to patients on the move to tracking patients and motion sensing for gaming controls. There has been significant and sizeable amount of research in the various areas and applications of body area networks. Ultra wideband which operates in the 3.1-10.6 GHz band is slowly being preferred for high data rate communication in body area networks. The development of suitable applications and techniques for communication depends significantly on the channel models. The wireless channel is a crucial parameter as it provides significant information about the propagation characteristics and losses involved in the transmission medium. The existing channel models proposed are mostly in the spectra involving the wideband 3.1-10.6 GHz bands or the 3.1-6 GHz bands. However, the IEEE 802.15.6 specifies operation in various sub-bands of 499.2 MHz width. And the channel characteristics are significantly different for wideband and narrowband channels. In this article, we propose empirical channel models for body surface communication in the various sub-bands specified by the IEEE 802.15.6. The body surface scenario is chosen as the combination of propagation through wireless media and losses due to absorption from body tissues make it challenging. The proposed path loss models are developed from more than 300,000 received power measurements collected over a span of hours.
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One of the well-established research domains among computer vision scientists is object tracking. However, not much work has been done in underwater scenarios. This article addresses the problem of visual tracking in the underwate...
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One of the well-established research domains among computer vision scientists is object tracking. However, not much work has been done in underwater scenarios. This article addresses the problem of visual tracking in the underwater environment with the stationary and nonstationary camera setups. In order to deal with the underwater optical dynamics, a dominant color component-based scene representation is employed in the YCbCr color space. An adaptive approach is devised to select the Walsh-Hadamard (WH) kernels for the efficient extraction of color, edge, and texture strengths, whereas a new feature called range strength is proposed to extract the variation of intensity from underwater sequences in the local neighborhood using the WH kernel. The likelihood of these feature strengths is integrated in a particle filter framework to track the object of interest in underwater sequences. The reference feature strengths used in assigning weights to the particles are updated based on the Sorensen distance. The coefficients of feature strengths are calculated in such a way that if one feature fails, then its coefficient become insignificant, whereas the more suitable features get higher feature coefficients. The effectiveness of the proposed scheme is evaluated using the underwater video datasets: reefVid, fish4knowledge (F4K), underwaterchangedetection (UWCD), and National Oceanic and Atmospheric Administration (NOAA). The performance evaluation is performed by comparing the scheme with five recent state-of-the-art tracking schemes. The quantitative analysis of the proposed scheme is carried out using three evaluation measures: overall intersection over union, centroid location error, and average tracking error. The performance of the proposed scheme is quite encouraging in the case of sequences with hazy and degraded, partially occluded, and camouflaged challenges.
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