Abstract
In this paper a novel approach is presented to detect moving object in H.264/AVC compressed domain for video surveillance applications. The proposed algorithm utilizes the information from the H.264 compressed bit stream to reduce the computational complexity and memory requirements. In order to exploit the spatial and temporal consistency of moving object, a Markov Random Field (MRF) model is employed to detect and segment moving object based on motion vectors and quantization parameters (QP). The size of the blocks (in bits) are also used to improve the detection result. Experiments show good performance achieved by the algorithm, and the moving object can be detected effectively from the compressed video sequence.
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References
Babu RV, Ramakrishnan KR, Srinivasan SH (2004) Video object segmentation: a compressed domain approach. IEEE Transactions on Circuits and Systems for Video Technology 14(4):462–474
Babu RV, Tom M, Wadekar P (2016) A survey on compressed domain video analysis techniques. Multimedia Tools and Applications 75(2):1043–1078
Besag J (1986) On the statistical analysis of dirty pictures. J R Stat Soc Ser B Methodol 48(3):259–302
Biswas S, Babu RV (2015) Anomaly detection in compressed H.264/AVC video. Multimed Tools Appl 74(24):11099–11115
Chen MY, Bajic IV, Saeedi PS (2011) Moving region segmentation from compressed video using global motion estimation and Markov random fields. IEEE Transactions on Multimedia 13(3):421–431
Cuevas C, Mohedano R, García N (2015) Statistical moving object detection for mobile devices with camera. IEEE international conference on consumer electronics, pp 15–16
I-Lids dataset for AVSS 2007. http://www.eecs.qmul.ac.uk/~andrea/avss2007_d.html
Kapotas SK, Skodras AN (2010) Moving object detection in the H.264 compressed domain. IEEE international conference on imaging systems and techniques, pp 325–328
Khatoonabadi SH, Bajić IV (2013) Video object tracking in the compressed domain using spatio-temporal Markov random fields. IEEE Transactions on Image Processing 22(1):300
Konda KR, Tefera YT, Conci N, Natale FGBD (2017) Real-time moving object detection and segmentation in H.264 video streams. IEEE international symposium on broadband multimedia systems and broadcasting, pp 1–6
Laumer M, Amon P, Hutter A, Kaup A (2015) Compressed domain moving object detection by spatio-temporal analysis of H.264/AVC syntax elements. Picture coding symposium(PCS), pp 282–286
Li, SZ (2001) Markov random field modeling in image analysis:xxiv+357
Liu L, Feng X, Ji R, Deng Y (2008) A moving object segmentation in MPEG compressed domain based on motion vectors and DCT coefficients. Image and signal processing. CISP '08. Congress on, vol 3. IEEE, pp 605–609
Liu S, Pan Z, Cheng X (2017) A novel fast fractal image compression method based on distance clustering in high dimensional sphere surface. Fractals-Complex Geometry Patterns and Scaling in Nature and Society 25(04):12-21
Mak CM, Cham WK (2009) Real-time video object segmentation in H.264 compressed domain. IET Image Process 3(5):272–285
Moridani AK, Fakhrmoosavy SH, Moridani MK (2015) Vehicle detection and tracking in roadway traffic analysis using Kalman filter. International Journal of Imaging and Robotics 15(2):45–52
Moriyama M, Minemura K, Wong KS (2016) Moving object detection in HEVC video by frame sub-sampling. International symposium on intelligent signal processing and communication systems, pp 48–52
Pan Z, Liu S, Fu W (2017) A review of visual moving target tracking[J]. Multimed Tools Appl 76(16):1–30
Poppe C, De Bruyne S, Paridaens T, Lambert P, Walle RVD (2009) Moving object detection in the H. 264/AVC compressed domain for video surveillance applications. J Vis Commun Image Represent 20(6):428–437
Praeter JD, Vyver JVD, Kets NV, Wallendael GV, Verstocket S (2017) Moving object detection in the HEVC compressed domain for ultra-high-resolution interactive video. IEEE international conference on consumer electronics, pp 135–136
Szczerba K, Forchhammer S, Stottrup-Andersen J, Eybye PT (2009) Fast compressed domain motion detection in H. 264 video streams for video surveillance applications. Advanced video and signal based surveillance. Sixth IEEE international conference, pp 478–483
Tom M, Babu RV (2013) Fast moving-object detection in H.264/AVC compressed domain for video surveillance. Computer vision, pattern recognition, image processing and graphics(NCVPRIPG), fourth National Conference, pp 1–4
Zeng W, Du J, Gao W, Huang Q (2005) Robust moving object segmentation on H.264/AVC compressed video using the block-based MRF model. Real-Time Imaging 11(4):290–299
Zhao L, He Z, Cao W, Zhao D (2017) Real-time moving object segmentation and classification from HEVC compressed surveillance video. IEEE Transactions on Circuits and Systems for Video Technology PP(99):1–1
Acknowledgments
The research is supported by the Natural Science Foundation of Inner Mongolia of China (No. 2014BS0602) and the Program of High-Level Talents of Inner Mongolia University (SPH-IMU).
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Ma, M., Song, H. Effective moving object detection in H.264/AVC compressed domain for video surveillance. Multimed Tools Appl 78, 35195–35209 (2019). https://doi.org/10.1007/s11042-019-08145-4
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DOI: https://doi.org/10.1007/s11042-019-08145-4