Abstract
This paper proposes a method of detecting moving objects using sequential inference of the background in a video taken with a moving camera. In the video taken using a moving camera, all positions of pixels change every frame. The positions of the background pixels in the image frame T are not the same as the positions of the background pixels in the image frame T + 1. 2D projective transform can be used to find changes in the pixel position every frame. Bilinear interpolation with four nearest pixels around the pixel in image frame T which corresponds to a pixel in the image frame T+1 can be used for creating a background model at T + 1. Having obtained the background model, a pixel in image frame T + 1 can be determined if it is a background pixel or a foreground pixel. The detection results of the proposed method are compared with the ground truth to determine the effectiveness of the proposed method.
This is a preview of subscription content, log in via an institution to check access.
Similar content being viewed by others
References
Kim IS, Choi HS, Yi KM, Choi JY, Kong SG (2010) Intelligent visual surveillance—a survey. Int J Control, Autom Syst 8(5):926–936
Joshi KA, Thakore DG (2012) A survey on moving object detection and tracking in video surveillance system. Int J Soft Comput Eng (IJSCE) 2(3):44–48
Bouwmans T (2011) Recent advance on statistical background modeling for foreground detection: a systematic survey. RCPS 4(3):147–176
Guyon C, Bouwmans T, Zahzah E (2012) Foreground detection based on low-rank and block-sparse matrix decomposition. In: Proceedings of the IEEE international conference on image processing (ICIP), pp 1225–1228
Stauffer C, Grimson WEL (1995) Adaptive background mixture models for real time tracking. Conf Comput Vis Pattern Recognit 2:246–252
Dahyot R (2006) Unsupervised camera motion estimation and moving object detection in videos. In: Proceedings of the Irish machine vision and image processing conference, pp 102–109
Jung B, Gourav SS (2004) Detecting moving objects using a single camera on a mobile robot in an outdoor environment. In: The 8th conference on intelligent autonomous systems, pp 980–987
Shobha G, Kumar NS (2012) Adaptive background modeling and foreground detection in video sequence using artificial neural network. International conference on intelligent computational systems (ICICS’2012), pp 22–25
Harris C, Stephens M (1988) A combined edge and corner detector. In: Proceedings of the 4th Alvey vision conference, pp 147–151
Lucas BD, Kanade T (1981) An iterative image registration technique with an application to stereo vision. In: Proceedings of the 7th international joint conference on artificial intelligence, pp 647–679
Fischler MA, Bolles RC (1981) Random sample consensus: a paradigm for model fitting with applications to image analysis and automated cartography. Commun ACM 24(6):381–395
Acknowledgments
This work was supported by JSPS KAKENHI Grant Number 25350477.
Author information
Authors and Affiliations
Corresponding author
Additional information
This work was presented in part at the 19th International Symposium on Artificial Life and Robotics, Beppu, Oita, January 22–24, 2014.
About this article
Cite this article
Setyawan, F.A., Tan, J.K., Kim, H. et al. Detecting moving objects from a video taken by a moving camera using sequential inference of background images. Artif Life Robotics 19, 291–298 (2014). https://doi.org/10.1007/s10015-014-0168-7
Received:
Accepted:
Published:
Issue date:
DOI: https://doi.org/10.1007/s10015-014-0168-7