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


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 \(({\text{p}}_{{{\text{tix}}}} )\) and about 90% of efficiency in overall system performance is increased.

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Correspondence to R. Nirmalan.

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Nirmalan, R., Gokulakrishnan, K. An intelligent surveillance video analytics framework using NACT-Hadoop/MapReduce on cloud services. Distrib Parallel Databases (2021).

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  • Real time systems
  • Performance
  • Video analytics
  • Distributed video processing
  • Hadoop
  • MapReduce