American Journal of Computing Research Repository
ISSN (Print): 2377-4606 ISSN (Online): 2377-4266 Website: Editor-in-chief: Vishwa Nath Maurya
Open Access
Journal Browser
American Journal of Computing Research Repository. 2014, 2(2), 28-32
DOI: 10.12691/ajcrr-2-2-1
Open AccessArticle

Developing Kinect-like Motion Detection System using Canny Edge Detector

Skander Benayed1, , Mohammed Eltaher1 and Jeongkyu Lee1

1Department of Computer Science and Engineering, University of Bridgeport, Bridgeport

Pub. Date: April 21, 2014

Cite this paper:
Skander Benayed, Mohammed Eltaher and Jeongkyu Lee. Developing Kinect-like Motion Detection System using Canny Edge Detector. American Journal of Computing Research Repository. 2014; 2(2):28-32. doi: 10.12691/ajcrr-2-2-1


With the advance in video technology, motion-based computing system has an effect on both hardware and software, such as video surveillance, security alarm systems and game entertainment. For example, Kinect is a webcam style add-on peripheral for Xbox 360 game console manufactured by Microsoft, which is featured by RGB camera and multi-array microphone. Kinect is a motion-based software technology that can provide 3-D motion detection, skeleton motion tracking, and voice and facial recognitions. It is currently considered the cutting edge in the gaming world. In this paper, we developed a Kinect-like motion detection system for video streams without using real Kinect device. There are many approaches of motion detection for video streams. However, most of them are based on frame-based comparisons, which could be resource intensive and make it challenging to keep up with the continuous need for speed. Therefore, we present a novel method for motion tracking and identification that are based on the Canny edge detector. The experimental results show that the method is fast and effective in simulating applications, such as Microsoft Kinect motion detection and video surveillance system.

Kinect Canny edge detection Fast Motion identification video processing

Creative CommonsThis work is licensed under a Creative Commons Attribution 4.0 International License. To view a copy of this license, visit


Figure of 6


[1]  Bowyer, K., Kranenburg, C., and Dougherty, S. Edge detector evaluation using empirical roc curve. Comput. Vision Image Understand.2001, pg.10, 77-103.
[2]  Canny, J., A Computational Approach To Edge Detection, IEEE Trans. Pattern Analysis and Machine Intelligence, 1986, pg.679-698.
[3]  Elena Deza and Michel Marie Deza, “Encyclopedia of Distances”, Springer, 2009, pg.94.
[4]  Gary Bradski & Adrian Kaehler. Learning OpenCV: Computer Vision with the OpenCV Library, O'REILLY, 2008.
[5], March 2012.
[6], March 2012.
[7], March 2012.
[8], March 2012.
[9], March 2012.