An Artificial Approach of Video Object Action Detection by Using Gaussian Mixture Model

An Artificial Approach of Video Object Action Detection by Using Gaussian Mixture Model

  IJETT-book-cover  International Journal of Engineering Trends and Technology (IJETT)          
  
© 2016 by IJETT Journal
Volume-42 Number-2
Year of Publication : 2016
Authors : Sneha Jain, Gagan Vishwakarma, Yogendra Kumar Jain
DOI :  10.14445/22315381/IJETT-V42P212

Citation 

Sneha Jain, Gagan Vishwakarma, Yogendra Kumar Jain "An Artificial Approach of Video Object Action Detection by Using Gaussian Mixture Model", International Journal of Engineering Trends and Technology (IJETT), V42(2),49-55 December 2016. ISSN:2231-5381. www.ijettjournal.org. published by seventh sense research group

Abstract
The digital data processing plays an important role in today’s world. This attracts many researchers to solve different issues of video object processing field. This paper works on one of significant issue of video objects detection with action recognition in videos and it proposes an artificial approach to detect action of the video object. The working methodology is consisting of the separation of foreground, background features in videos and the training of the system. Foreground and background features of the videos are separated by using gaussian mixture model. The training and action recognition of detected object is done by error back propagation neural network. The experiments are carried out on real datasets, which further calculate results of comparisons. The comparisons are taken out between experimental results and existing action detection methods. Results show that proposed work reduces the execution time and increases the pixel localization parameter.

 References

[1]. Rodriguez, M., Sivi. C., J., Laptev, I., and Audibert, J.Y., “Data-Driven Crowd Analysis In Videos”, 13th International Conference On Computer Vision, Barcelona, ICCV 2011, Pages1235-1242, 2011.
[2]. Fan, Q., Gabbur, P., and Pankanti, S., “Relative Attributes For Largescale Abandoned Object Detection”, 14th International Conference On Computer Vision, Australia, Pages 2736–2743, 2013.
[3]. Chatfield, K., Simonyan, K., Vedaldi, A., And Zisserman, A., “Return Of The Devil In The Details: Delving Deep Into Convolutional Nets” Technical Report, University Of Oxford. Archived In Arxiv Pages 1405-3531, 2014.
[4]. Wang, H., Kl Aser, A., Schmid, C., And Liu, C.L., “Dense Trajectories And Motion Boundary Descriptors For Action Recognition”, International Journal Of Computer Vision, Vol. 103(1), Pages 60-79, 2013.
[5]. C. Stauffer And W. E. L. Grimson, “Adaptive Background Mixture Models For Real-Time Tracking In Computer Vision And Pattern Recognition”, IEEE Computer Society, Pages 2246–2252, 1999.
[6]. Viswanath Gopalakrishnan, Deepu Rajan, And Yiqun Hu, “A Linear Dynamical System Framework For Salient Motion Detection”, IEEE Transactions On Circuits And Systems For Video Technology, Vol. 22, NO. 5, Page 683, MAY 2012.
[7]. W.T. Lee And H. T. Chen, “Histogram-Based Interest Point Detectors”, IEEE Conference On Computer Vision And Pattern Recognition, Pages 1590-1596, 2009.
[8]. Rupesh Kumar Rout, “A Survey On Object Detection and Tracking Algorithms”, Department Of Computer Science And Engineering National Institute Of Technology Rourkela Rourkela 769 008, India.
[9]. Jiuyuehao, Chao Li, Zuwhan Kim, and Zhang Xiong, “Spatio-Temporal Traffic Scene Modeling For Object Motion Detection”, IEEE Intelligent Transportation Systems, 2012.
[10]. Liu Gangl, Ningshangkun, You Yugan, Wen Guanglei And Zhengsiguo, “An Improved Moving Objects Detection Algorithm”, IEEE International Conference On Wavelet Analysis And Pattern Recognition, Pages 96-102, 14-17 July, 2013.
[11]. Idrees, H., Warner, N., And Shah, M., “Tracking In Dense Crowds Using Prominence And Neighborhood Motion Concurrence”, Image And Vision Computing, Vol. 32(1), Pages 14–26, 2014.
[12]. Zhong Zhou, Member, Feng Shi, And Wei Wu., “Learning spatial And Temporal Extents Of human Actions For Action Detection”, DOI 10.1109/TMM.2015.2404779, IEEE Transactions On Multimedia, 2015.
[13]. Sivabalakrishnan, M., Manjula D., “Adaptive Background Subtraction In Dynamic Environments Using Fuzzy Logic”, International Journal On Computer Science And Engineering, Vol. 2, Pages 270–273, 2010.
[14]. Elgammal, A., Duraiswami, R., Harwood, D., Davis, L., “Background And Foreground Modeling Using Nonparametric Kernel Density Estimation For Visual Surveillance”, IEEE 90, Pages 1151 – 1163, 2002.
[15]. Yang Cong, Junsong Yuan, and Yandong Tang, “Video Anomaly Search in Crowded Scenes via Spatio-Temporal Motion Context”, IEEE Transactions On Information Forensics And Security, Vol. 8, No. 10, October 2013.

Keywords
Action detection, Feature extraction, Image processing, Object tracking, Neural network.