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27th Conference on Innovation in Clouds, Internet and Networks (ICIN)

作者： Techasarntikul, Nattaon Shimonishi, Hideyuki Murata, Masayuki Osaka Univ Osaka Japan

ISBN: (纸本)9798350393767;9798350393774

Transmitting a high data rate video to the cloud for real-time processing purpose requires minimizing the latency, maximizing the application requirements, and optimizing power consumption for the entire system. In this study, we employed a distributed video processing model for an object detection task, assuming that video streams are captured by robots operating in the licensed 28 GHz Milliwave network, ensuring the stability of video uploads. Through the optimization of power consumption, the system efficiently allocated video analysis frames to appropriate devices, resulting in an 18% decrease in overall power usage.

关键词： distributed video processing 28 GHz Millimeter-wave object detection

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An intelligent surveillance video analytics framework using NACT-Hadoop/MapReduce on cloud services

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distributed AND PARALLEL DATABASES 2021年第4期39卷 873-889页

作者： Nirmalan, R. Gokulakrishnan, K. Bannari Amman Inst Technol Dept Comp Sci & Engn Sathyamangalam Tamil Nadu India Anna Univ Dept Elect & Commun Engn Reg Campus Tirunelveli Tamil Nadu India

video analytics has gradually increased in recent years. The intelligent CCTV cameras in public places, you-tube videos, etc. generate an enormous amount of video data. Generally, video analytics required more time as it contains several processes like encoding, decoding, etc. There are several existing approaches are evolved in improving the efficiency of video analytics but performance delay and loss of data still existing challenges. With our analysis, we strongly state VM migration will be an effective solution to overcome this delay and performance issues. In this paper, we propose NACT based map reducing mechanism (NACT-Map) for processing the real-time streaming videos. The NACT (Novel Awaiting Computation Time) enables the prediction of VM allocation and automatic migration. The scheduling and allocating of the optimal resource are done by task monitor who utilizes the Task manager (TM) system. The NACT based VM migration and MapReduce technique with Hadoop simplifies the process and minimizes the execution time. The splitting of video into chunks of frames speedup the process. Further efficiency is improved by the Map Reduce technique which uses video and its related content for clusters. The performance of our proposed system is executed in the cloudsim with a large dataset contains two real-time videos. Further, the result is compared with the existing methodologies such as distributed video decoding mechanism with extended FFmpeg and videoRecordReader (VDMFF) (Yoon et al. in distributed video decoding on Hadoop. IEICE Trans Inf Syst E101-D(1):2933-2941, 2018) and distributed video Analytics Framework for Intelligent video Surveillance (SIAT) (Uddin et al. in SIAT: a distributed video analytics framework for intelligent video surveillance. Symmetry 11:911, 2019). The obtained result shows our proposed NACT_Map consumes minimum Task processing time (p(tix)) and about 90% of efficiency in overall system performance is increased.

关键词： Real time systems Performance video analytics distributed video processing Hadoop MapReduce

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distributed video Decoding on Hadoop

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IEICE TRANSACTIONS ON INFORMATION AND SYSTEMS 2018年第12期E101D卷 2933-2941页

作者： Yoon, Illo Yi, Saehanseul Oh, Chanyoung Jung, Hyeonjin Yi, Youngmin Univ Seoul Sch Elect & Comp Engn Seoul South Korea

video analytics is usually time-consuming as it not only requires video decoding as a first step but also usually applies complex computer vision and machine learning algorithms to the decoded frame. To achieve high efficiency in video analytics with ever increasing frame size, many researches have been conducted for distributed video processing using Hadoop. However, most approaches focused on processing multiple video files on multiple nodes. Such approaches require a number of video files to achieve any speedup, and could easily result in load imbalance when the size of video files is reasonably long since a video file itself is processed sequentially. In contrast, we propose a distributed video decoding method with an extended FFmpeg and videoRecordReader, by which a single large video file can be processed in parallel across multiple nodes in Hadoop. The experimental results show that a case study of face detection and SURF system achieve 40.6 times and 29.1 times of speedups respectively on a four-node cluster with 12 mappers in each node, showing good scalability.

关键词： distributed video processing Hadoop extended FFmpeg

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Efficient Online Surveillance video processing Based on Spark Framework 2nd

Efficient Online Surveillance Video Processing Based on Spar...

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2nd International Conference on Big Data Computing and Communication (BigCom)

作者： Zhang, Haitao Yan, Jin Kou, Yue Beijing Univ Posts & Telecommun Beijing Key Lab Intelligent Telecommun Software & Beijing 100876 Peoples R China

ISBN: (纸本)9783319425535;9783319425528

In the current surveillance video processing systems, the video processing algorithms and the physical resources are highly coupled, and a video stream is usually used as a basic task scheduling unit. With the expansion of the scale of the system, the traditional systems will cause the large resource fragments that cannot be utilized adequately. In this paper, we propose a novel online surveillance video processing system architecture that combines the distributed Kafka message queue and Spark computing framework. Our system decouples the video stream collection and the video stream processing, and further decouples the video processing tasks and the physical resources. This loosely coupled architecture can quickly recover the failed tasks without data loss for the large-scale video surveillance, and can provide the more scalable distributed computing ability. In addition, a fine-grained online video task management method, which uses the cached video data blocks as the scheduling units, is proposed to increase the resource utilization. Experimental results show that our system has the higher resource utilization and the higher task capacity compared with the traditional systems.

关键词： video surveillance distributed video processing Spark Message queue

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HVPI: Extending Hadoop to Support video Analytic Applications 8

HVPI: Extending Hadoop to Support Video Analytic Application...

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IEEE 8th International Conference on Cloud Computing

作者： Zhao, Xiaomeng Ma, Huadong Zhang, Haitao Tang, Yi Kou, Yue Beijing Univ Posts & Telecommun Beijing Key Lab Intelligent Telecommun Software & Beijing Peoples R China

ISBN: (纸本)9781467372879

Hadoop is widely deployed distributed computing framework and makes creating distributed applications much easier. However, unlike text data, there is no existing video r/w interface for Hadoop, and many existing video analytic applications implemented in C/C++ are not compatible with Hadoop framework. In this paper, we propose an open source Hadoop video processing interface HVPI to extend Hadoop to support video analytic applications. It provides easy-to-use video r/w interface for developers to quickly build large-scale video analytic applications based on Hadoop, and native processing interface to help users easily port existing video analytic applications written in C/C++ into Hadoop platform. We also present two typical use cases of HVPI and do experiments based on them. Experimental results demonstrate that the applications built based on HVPI are both scalable and efficient.

关键词： hadoop video analysis distributed video processing JNI hadoop steaming hadoop pipes

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HVPI: Extending Hadoop to Support video Analytic Applications

HVPI: Extending Hadoop to Support Video Analytic Application...

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IEEE International Conference on Cloud Computing

作者： Xiaomeng Zhao Huadong Ma Haitao Zhang Yi Tang Yue Kou Beijing Key Lab of Intelligent Telecommunication Software and Multimedia Beijing University of Posts and Telecommunications China

关键词： Hadoop video analysis distributed video processing JNI Hadoop steaming Hadoop pipes

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Activity Based Matching in distributed Camera Networks

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IEEE TRANSACTIONS ON IMAGE processing 2010年第10期19卷 2595-2613页

作者： Ermis, Erhan Baki Clarot, Pierre Jodoin, Pierre-Marc Saligrama, Venkatesh Boston Univ Boston MA 02215 USA Univ Sherbrooke Sherbrooke PQ J1K 2R1 Canada

In this paper, we consider the problem of finding correspondences between distributed cameras that have partially overlapping field of views. When multiple cameras with adaptable orientations and zooms are deployed, as in many wide area surveillance applications, identifying correspondence between different activities becomes a fundamental issue. We propose a correspondence method based upon activity features that, unlike photometric features, have certain geometry independence properties. The proposed method is robust to pose, illumination and geometric effects, unsupervised (does not require any calibration objects). In addition, these features are amenable to low communication bandwidth and distributed network applications. We present quantitative and qualitative results with synthetic and real life examples, and compare the proposed method with scale invariant feature transform (SIFT) based method. We show that our method significantly outperforms the SIFT method when cameras have significantly different orientations. We then describe extensions of our method in a number of directions including topology reconstruction, camera calibration, and distributed anomaly detection.

关键词： Activity pattern matching anomaly detection compressive sensing distributed video processing multicamera networks

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