Object-based on-line video summarization for internet of video things

Lin, Shih-Ting; Liao, Yuan-Hsin; Tsao, Yu; Chien, Shao-Yi

doi:10.1109/iscas.2017.8050237

Cited by 8 publications

(8 citation statements)

References 9 publications

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“…Image with a high-cost box is summarized and utilized for video retrieval on search, which reduces the time, memory, and computation requirements of the system. Lin et al [113] proposed a multi-view video summarization method to address the high bandwidth transmission requirement for the Internet of Video Things (IoVT). The proposed scheme is based on the K-Nearest Neighbour (KNN) model.…”

Section: D: Surveillance For Public Safetymentioning

confidence: 99%

Multimedia Internet of Things: A Comprehensive Survey

et al. 2020

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The immense increase in multimedia-on-demand traffic that refers to audio, video, and images, has drastically shifted the vision of the Internet of Things (IoT) from scalar to Multimedia Internet of Things (M-IoT). IoT devices are constrained in terms of energy, computing, size, and storage memory. Delaysensitive and bandwidth-hungry multimedia applications over constrained IoT networks require revision of IoT architecture for M-IoT. This paper provides a comprehensive survey of M-IoT with an emphasis on architecture, protocols, and applications. This article starts by providing a horizontal overview of the IoT. Then, we discuss the issues considering the characteristics of multimedia and provide a summary of related M-IoT architectures. Various multimedia applications supported by IoT are surveyed, and numerous use cases related to road traffic management, security, industry, and health are illustrated to show how different M-IoT applications are revolutionizing human life. We explore the importance of Quality-of-Experience (QoE) and Quality-of-Service (QoS) for multimedia transmission over IoT. Moreover, we explore the limitations of IoT for multimedia computing and present the relationship between the M-IoT and emerging technologies including event processing, feature extraction, cloud computing, Fog/Edge computing and Software-Defined-Networks (SDNs). We also present the need for better routing and Physical-Medium Access Control (PHY-MAC) protocols for M-IoT. Finally, we present a detailed discussion on the open research issues and several potential research areas related to emerging multimedia communication in IoT. INDEX TERMS Multimedia Internet of Things (M-IoT), multimedia communication, Internet of Multimedia Things (IoMT), multimedia computing, Quality-of-Experience (QoE), Quality-of-Service (QoS), multimedia routing, medium access control (MAC).

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Section: D: Surveillance For Public Safetymentioning

confidence: 99%

Multimedia Internet of Things: A Comprehensive Survey

et al. 2020

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“…Increased network latency can cause unpredictable behaviour in applications which require real-time network constraints. The number of applications which involve real-time video acquisition and broadcasting over the Internet has increased in recent years [7] and the 2 Wireless Communications and Mobile Computing specific nature and often low-latency requirements of these applications has led to the establishment of a special branch of the Internet of Things (IoT) called the Internet of Video Things (IoVT) [8,9].…”

Section: Introductionmentioning

confidence: 99%

Real‐Time Video Latency Measurement between a Robot and Its Remote Control Station: Causes and Mitigation

Kaknjo

Rao

Omerdić

et al. 2018

Wireless Communications and Mobile Computing

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This work presents a detailed study, characterization, and measurement of video latency in a real-time video streaming application. The target application consists of an automatic control system in the form of a control station and the mini Remotely Operated Vehicle (ROV) equipped with a camera, which is controllable over local area network (LAN) and the Internet. Control signal transmission and feedback measurements to the operator usually impose real-time constraints on the network channel. Similarly, the video stream, which is required for the normal system control and maneuvering, imposes further strict requirements on the network in terms of bandwidth and latency. Based on these requirements, controlling the system in real time through a standard Internet connection is a challenging task. The measurement of important network parameters like availability, bandwidth, and latency has become mandatory for remotely controlling the system in real time. It is necessary to establish a methodology for the measurement of video and network latency to improve the real-time controllability and safety of the system as such measurement is not possible using existing solutions due to the following reasons: insufficient accuracy, relying on the Internet resources such as generic Network Time Protocol (NTP) servers, inability to obtain one-way delay measurement, and many solutions only having support for web cameras. Here, an efficient, reliable, and cost-effective methodology for the measurement of latency of a video stream over a LAN and the Internet is proposed. A dedicated stratum-1 NTP server is used and the necessary software needed for acquiring and measuring the latency of a video stream from a generic IP camera as well as integration into the existing ROV control software was developed. Here, by using the software and dedicated clock synchronization equipment (NTP server), it was found that normal video latencies in a LAN were in the range of 488ms -850ms, while latencies over the Internet were measured to be in the range of 558ms -1211ms. It is important to note that the values were obtained by using a generic (off-the-shelf) IP camera and they represent the actual latencies which might be experienced during control over long range and across international territory borders.

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“…The design challenges of these visual sensor-equipped IoVT edge nodes are high-energy consumption and high-transmission bandwidth requirement, as well as a high-power consumption for data transmission and communication [13]. Though video and image compression techniques can significantly reduce the required bandwidth, it is still high when compared to the lightweight requirement of common IoT nodes [13], [36] (e.g., 10 Mbps for 1080p over H264 vs. 1 byte of a temperature sensor reading). Hence, an IoVT edge node may require relatively higher energy resources, computational power, and communication capability.…”

Section: B Edge-node Architecturementioning

confidence: 99%

“…In another study [10], the authors estimated that the number of cameras would rise to 13 billion by 2030. Therefore, there is a need to combine visual sensors and surveillance data within the framework of the IoT that will require higher bandwidth and power for transmission and computational ability [11]- [13]. The conventional cloud-based architecture alone is not capable of dealing with the issues that are related to the fast-growing number of visual IoT nodes in the IoVT paradigm which has led to the introduction of fog and edge computing.…”

Section: Introductionmentioning

confidence: 99%

Choice of Application Layer Protocols for Next Generation Video Surveillance Using Internet of Video Things

Sultana

Wahid

2019

IEEE Access

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Video surveillance has become ubiquitous due to the increasing security requirements in every sphere of life. The next generation video surveillance system (VSS) possesses great challenges in various applications, such as intelligent urban surveillance systems and smart cities. In these applications, we need to deal with the fast-growing number of surveillance nodes which introduce several constraints, e.g., high latency, high bandwidth, high energy consumption, and CPU and memory usage. To address these issues, the Internet of Video Things (IoVT), which is considered to be a part of the Internet of Things (IoT), can be a solution. The IoVT is composed of visual sensors (i.e., cameras) connected to the Internet. Unlike conventional systems, the VSS under an IoVT framework provides multiple layers (i.e., edge, fog, and cloud) of communication and decision making by capturing and analyzing rich contextual and behavioral information. Since an appropriate application layer protocol (ALP) can help in alleviating the challenges of future VSSs, the selection of ALPs is important for IoVT-based systems. Therefore, this paper presents a generic architecture of an IoVT-based VSS and a comparative analysis of several ALPs, such as MQTT, AMQP, HTTP, XMPP, CoAP, and DDS, with real-time experimentation. This analysis will assist the users to choose the appropriate ALPs in various surveillance applications and determine their suitability at different nodes of the IoVT framework.INDEX TERMS Application layer protocols, the Internet of Video Things (IoVT), video analytics, video surveillance.

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Object-based on-line video summarization for internet of video things

Cited by 8 publications

References 9 publications

Multimedia Internet of Things: A Comprehensive Survey

Multimedia Internet of Things: A Comprehensive Survey

Real‐Time Video Latency Measurement between a Robot and Its Remote Control Station: Causes and Mitigation

Choice of Application Layer Protocols for Next Generation Video Surveillance Using Internet of Video Things

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