Abstract
This work presents a unique approach to utilize the existing video surveillance infrastructure to optimize electricity consumption in large indoor spaces such as library reading halls, waiting rooms, indoor sports complex, large dormitories etc. The proposed method extracts features from the encoder loop of the digital video recorder (DVR) to analyze the foreground activity. High Efficiency Video Coding (HEVC) is chosen as the target video codec, as it is going to be the most widely deployed video codec in the future DVRs. The bit rate efficiency of HEVC is almost double in comparison to H.264/AVC for same video quality [17]. The proposed method utilizes only compressed domain parameters from video stream such as motion vectors clustering information, block partitioning modes etc., which allows to develop algorithms with relatively lower computational complexity and memory requirement than pixel based methods thus achieving real time performance.
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References
Babu RV, Tom M, Wadekar P (2016) A survey of compressed domain video analysis techniques. In:. Multimedia tools applic 75:1043–1078
Barnich O, Van Droogenbroeck M (2009) Vibe: a powerful random technique to estimate the background in video sequences. In: IEEE international conference on acoustics, speech and signal processing, pp. 945–948
Chan YW, Chen K, Yuan S, Kuo AS (2016) Moving object counting using a tripwire in H.265/ HEVC bitstreams for video surveillance. In:. IEEE Access 4:2529–2541
Dey B, Kundu M (2015) Efficient foreground extraction from HEVC compressed video for application to real time analysis of surveillance big data. In:. IEEE Trans Circuits Syst Video Techno 24(11):3574–3585
DS-7304/7308/7316HQHI-K4, Turbo HD DVR, Hangzhou Hikvision Digital Technology Co., Ltd
Elgammal A, Hanvood D, Davis LS (2000) Non-parametric model for background subtraction. Proc ECCV 2000:751–767
Goyette N, Jodoin P-M, Porikli F, Konrad J, Ishwar P (2014) A novel video dataset for change detection benchmarking. In:. IEEE Trans Image Process 23(11):4663–4679
HEVC Test Model version 16.12 https://hevc.hhi.fraunhofer.de/
Hofman M, Tiefenbacher P, Rigoll G (2012) Background segmentation with feedback: the pixel based adaptive segmenter. In: Proc. IEEE computer society conference on computer vision and pattern recognition workshop, pp. 38–43
Huang T, Dong S, Tian Y (2014) Representing visual objects in HEVC coding loop. In:. IEEE J emerging and selected topics in circuits and systems 4(1):5–16
Kim K, Chalidabhongse T, Harwood D, Davis L (2004) Background modeling and subtraction by codebook construction. In: Int. conference on image processing, pp. 3061–3064
Li H, Zhang Y, Yang M, Men Y, Chao H (2014) A rapid abnormal event detection method for surveillance videos based on a novel feature in compressed domain of HEVC. In: IEEE conf. Multimedia Expo
Liang Y, Xu M, Ren J, Wang Z (2015) Learning to segment videos in HEVC compressed domain. In: International conf. wireless comm. signal processing
Maddalena L, Petrosino A (2008) A self organizing approach to background subtraction for visual surveillance applications. In: IEEE trans. Image processing, 1168–1177
Moriyama M, Minemura K, Wong K (2015) Moving object detection in HEVC video by frame sub sampling. In: International sympo. on intell. signal proces. comm. syst. pp. 48–52
Stauffer C, Grimson E (1999) Adaptive background mixture models for real time tracking. In:. Proc IEEE International conference on computer vision and pattern recognition 2:246–252
Sullivan GJ, Ohm J, Han W-J, Wiegand T (2012) Overview of the high efficiency video coding (HEVC) standard. In:. IEEE Trans Circuits Syst Video Technol 22(12):1649–1668
Sze V, Budagavi M, Sullivan GJ (2014) High efficiency video coding (HEVC), algorithms and architectures, Springer Switzerland
Wang H, Suter D (2006) Background subtraction based on a robust consensus method. In: Proc. Of 18th international conference on pattern recognition, pp. 223–226
Wang W, Yang J, Gao W (2008) Modelling background and segmenting moving objects from compressed videos. In:. IEEE transactions on circuit & systems for video technology 18(5):670–681
Xu Y, Dong J, Zhang B, Xu D (2016) Background modeling methods in video analysis: A review and comparative evaluation. In: CAAI transactions on intelligent technology, 1
Zhao L, He Z, Cao W, Zhao D (2017) Real time moving object segmentation and classification from HEVC compressed surveillance videos. In: IEEE Trans. Circuits Syst. Video Techno. preprint issue 99
Zivkovic Z (2004) Improved adaptive gaussian mixture model for background subtraction. In: Proc. 17th international conference on pattern recognition, vol. 2, pp. 28–31
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Samaiya, D., Gupta, K.K. Intelligent video surveillance for real time energy savings in smart buildings using HEVC compressed domain features. Multimed Tools Appl 77, 29059–29076 (2018). https://doi.org/10.1007/s11042-018-6087-1
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DOI: https://doi.org/10.1007/s11042-018-6087-1