Advertisement

Efficient Storage and Processing of Video Data for Moving Object Detection Using Hadoop/MapReduce

  • Jyoti Parsola
  • Durgaprasad Gangodkar
  • Ankush Mittal
Conference paper
Part of the Lecture Notes in Electrical Engineering book series (LNEE, volume 395)

Abstract

Technological advances and easy availability of low cost video camera has further encouraged users for deploying network of surveillance systems. These systems generate massive data. Thus, storage and processing of the huge video data for application such as video forensics, post event investigation etc., has emerged as a challenging problem to the research community. In this paper we propose a powerful approach that makes use of Hadoop Distributed File System (HDFS) for efficient storage of video data and programming model called MapReduce for data intensive computing. The proposed approach detects moving objects and provides their coordinates which can be used for localizing post event investigation. We have analyzed the storage and processing of video data of varying resolution and size to assess the performance of proposed approach.

Keywords

Motion detection Hadoop distributed file system MapReduce 

References

  1. 1.
    Dean, J. and Ghemawat, S.: Map Reduce: Simplified Data Processing on Large Cluster. Operating Systems Design and Implementation (OSDI). San Francisco, United States (2004)137–150.Google Scholar
  2. 2.
    Pereira, R. and Breitman, K.: A Cloud Based Architecture for Improving Video Compression Time Efficiency: The Split &Merge Approach. 3rd IEEE Int. Conf. on Cloud Computing (CLOUD). Miami, United States July (2010) 482–489.Google Scholar
  3. 3.
    Schmidt, R. and Rella, M.: An approach for processing large and non-uniform media objects on MapReduce-based clusters. 13th Int. Conf. on Asia-Pacific Digital Libraries, (ICADL). Beijing, China (2011).Google Scholar
  4. 4.
    Tan, H. and Chen, L.: An approach for fast and parallel video processing on Apache Hadoop clusters. IEEE Int. Conf on Multimedia and Expo (ICME). Chengdu, China (2014) 1–6.Google Scholar
  5. 5.
    Yamamoto, M. and Kaneko, K.: Parallel image database processing with MapReduce and performance evaluation in pseudo distributed mode. Int. J. of Electronic Commerce Studies, Vol. 3, No. 2 (2012) 211–228.Google Scholar
  6. 6.
    Ryu, C., Lee, C. M. Jhang, Kim, C., Seo, E.: Extensible video processing framework in apache hadoop. in Proc. IEEE 5th Int. Conf on Cloud Computing Technology and Science (CloudCom), Vol. 2, Bristol, England, Dec (2013) 305–310.Google Scholar
  7. 7.
    Zhang, W., Xu, L., Duan, P., Gong, W., Liu, X. and Q. Lu.: Towards a High Speed Video Cloud Based on Batch Processing Integrated with Fast Processing. in Proc. of Int. Conf of Identification, Information and Knowledge in the Internet of Things (IIKI), Beijing, China (2014) 28–33.Google Scholar
  8. 8.
    Zhao, X., H. Ma, H., Zhang, H., Tang, Y. and Fu, G.: Metadata extraction and correction for large-scale traffic surveillance videos. in Proc. on Big Data (Big Data), Washington, DC, United States (2014) 412–420.Google Scholar
  9. 9.
    Zhu, H., Shen, Z., Shang, L. and Pang, X.: Parallel Image Texture Feature Extraction under Hadoop Cloud Platform. Intelligent Computing Theory. Springer International Publishing (2014) 459–465.Google Scholar
  10. 10.
    Premchaiswadi, W., Tungkatsathan, A., Intarasema, S. and Premchaiswadi, A.: Improving performance of content-based image retrieval schemes using Hadoop MapReduce. In Int. Conf. of High Performance Computing and Simulation (HPCS). Helsinki, Finland (2013) 615–620.Google Scholar
  11. 11.
    Gamage, T.D, Samarvikrama, J.G., Rodrigo, R.and Pasqual, A. A.: Image filtering with MapReduce in pseudo-distribution mode. in IEEE Conf. of Moratuwa Engineering Research Conference (MERCon). Moratuwa, Sri Lanka (2015) 160–164.Google Scholar
  12. 12.
    APACHE, 2010. Hadoop MapReduce framework. Available: http://hadoop.apache.org/mapreduce/.
  13. 13.
    T. White. Hadoop: “The Definitive Guide”. Yahoo Press, 2010.Google Scholar
  14. 14.
    Holmes, Alex, “ Hadoop in practice”, Manning Publications Co., 2012.Google Scholar
  15. 15.
    Grimson, W.E.L. and Stauffer, C.: Adaptive background mixture models for real-time tracking, in Proc. Of IEEE Conf. Computer Vision and Pattern Recognition, Vol. 2, Fort Collins, United States (1999) 22–29.Google Scholar
  16. 16.
    Lim, S., Apostolopoulos, J.G. and Gamal, A.E.: Optical flow estimation using temporally oversampled video, IEEE Trans. of Image Processing, Vol. 14, No. 8(2005) 1074–1087.Google Scholar
  17. 17.
    Yu, Y. and Chen, Y.: A real-time motion detection algorithm for traffic monitoring systems based on consecutive temporal difference. 7th Conf of Asian Control Conference (ACC). Hong Kong, China (2009) 1594–1599.Google Scholar
  18. 18.
    Gangodkar, D., Kumar, P. and Mittal, A.: Segmentation of moving objects in visible and thermal videos. In Int. Conf of Computer Communication and Informatics (ICCCI), Coimbatore, India (2012) 1–5.Google Scholar
  19. 19.
    Open Source Computer Vision (OpenCV) [Online]. Available: http://opencv.willowgarage.com/wiki/.
  20. 20.
    Performance Evaluation of Tracking and Surveillance, (PETS). Available: http://www.cvg.cs.rdg.ac.uk/slides/pets.html.
  21. 21.
    Ecole Polytechnique, Montreal, LITIV Datasets Available: http://www.polymtl.ca/litiv/en/vid/index.php.
  22. 22.
    OTCBVS Benchmark Dataset Collection, Ohio State University. Available: http://www.cse.ohio-state.edu/otcbvs-bench/.
  23. 23.
    Context Aware Vision Using Image-Based Active Recognition (CAVIAR).Available: http://groups.inf.ed.ac.uk/vision/CAVIAR/.html.

Copyright information

© Springer India 2017

Authors and Affiliations

  • Jyoti Parsola
    • 1
  • Durgaprasad Gangodkar
    • 2
  • Ankush Mittal
    • 2
  1. 1.Department of Computer ApplicationsGraphic Era UniversityDehradunIndia
  2. 2.Department of Computer Science and EngineeringGraphic Era UniversityDehradunIndia

Personalised recommendations