Call for Paper - January 2023 Edition
IJCA solicits original research papers for the January 2023 Edition. Last date of manuscript submission is December 20, 2022. Read More

A Comparison between Moving Object Detection Methods Including a Novel Algorithm Used for Industrial Line Application

International Journal of Computer Applications
Foundation of Computer Science (FCS), NY, USA
Year of Publication: 2016
Muhamad Azhar Abdilatef

Muhamad Azhar Abdilatef. A Comparison between Moving Object Detection Methods Including a Novel Algorithm Used for Industrial Line Application. International Journal of Computer Applications 152(1):20-28, October 2016. BibTeX

	author = {Muhamad Azhar Abdilatef},
	title = {A Comparison between Moving Object Detection Methods Including a Novel Algorithm Used for Industrial Line Application},
	journal = {International Journal of Computer Applications},
	issue_date = {October 2016},
	volume = {152},
	number = {1},
	month = {Oct},
	year = {2016},
	issn = {0975-8887},
	pages = {20-28},
	numpages = {9},
	url = {},
	doi = {10.5120/ijca2016911753},
	publisher = {Foundation of Computer Science (FCS), NY, USA},
	address = {New York, USA}


In this paper a comparison between many types of moving object detection methods is presented, one of these used methods is proposed depending on a combination between many detection methods with many enhancements. A novel method is proposed depended on a combination between one of the previous method with some enhancement and the edges technique. All of these methods are applied to detect three types of objects their movement. The objects are moved through a conveyer belt, thus these movements have a known speed and direction. The objects have to be griped and moved to a known location using a robot arm. The most important issue here is how to detect the objects according to their details in order to grip and move them to other location in a real time; the processing time, the robot movement time inside the images capturing time must be adjacent to zero, Thus it is important to apply many well methods over the same environment for the same types of objects to compare between the methods and select the best one to be used according to the time and the detection accuracy. The objects are detected by a stationary camera mounted with the conveyer belt, it is used to see any changes happened inside the used conveyer, thus a real time video using this camera is recorded immediately and the video’s frames those included some changes (objects detected) have to be sent to a processing unit (Matlab code in pc); when the process operation is completed, the objects will be selected according to their features to be moved to other location, a robot arm (type Rios) is used here for gripping the selected objects (during their movement) to a known location. The comparison between the used detection methods is done according to the processing time and the detection accuracy. Firstly the used moving object detection methods are classified into two groups according to their processing time, the best group that has the lowest processing time will be selected to be used in real time applications, and then the best detection accuracy method in this selected group has to be used for the application. The (PCC) Percentage Correct Classification is used as a selection factor to choose the best method in the selected group. The obtained results shown that the new proposed method (Morphological Operation with the Prewitt Edge Detection with the region of interest), is selected according to its low processing time and high detection accuracy to be used for the industrial application.


  1. Xing Wang, 2007, “Laplacian Operator- Based Edges Detectors”, IEEE Trans. Pattern Analysis and Machine Intelligence, Vol 29, No. 15 pp. 886-890.
  2. Albiol A., Sanchis L., Albiol A. , Mossib J., 2011, “Detection of Parked Vehicles Using Spatiotemporal Map”, IEEE Trans. Intelligent Transportation Systems ,Vol. 12, No. 4, pp. 1277-1291.
  3. Heikkila J., Silvan O., 1999, “A Real – Time System for Monitoring of Cyclists and Pedestrians”, Second IEEE Workshop on Visual Surveillance, pp. 74-81.
  4. Gonzalez R., Woods R.,(2001, “Digital Image Processing”, Second Edition, Prentice Hall, University of Tennessee, New Jersey, pp.45-67.
  5. Thompson W., Lechleider P., Stuck E., 1993, “Detecting Moving Objects Using the Rigidity Constraint”, IEEE Trans. Pattern Analysis and Machine Intelligence, Vol. 15, No. 2, pp. 162-166.
  6. Wanhyun C., Sunworl K., Gukdong A., Sangcheol P., (2012),“Detecting and Tracking of Multiple Moving Objects in Video Sequence Using Entropy Mask Method and Fast Level Set Method”, IEEE, Vol. 1, pp.1-6 .
  7. Cuevas C., Mohedano R., Jaureguizar F., Garcı´a N., 2010, “High-Quality Real-Time Moving Object Detection by Non-Parametric Segmentation”, Electronics Letters, Vol. 46, No. 13, pp.910-911.
  8. Seungwon L., Lee J., Hayes M., Paik J., 2012, “Adaptive Background Generation for Automatic Detection of Initial Object Region in Multiple Color-Filter Aperture Camera- Based surveillance System”, IEEE Trans. Consumer Electronics, Vol. 58, No. 1, pp. 104-110.
  9. Foresti, 1998, “A Real -Time System for Video Surveillance of Unattended Outdoor Environments”, IEEE Trans. Circuits and Systems for Video Technology, Vol. 8, No. 6, pp. 697-704.
  10. Wei Liu, Hongfei Yu, Huai Yuang, Hong Zhao, and Xiaowei Xu, 2015, “Effective background modelling and Subtraction approach for moving object detection”, IETComputer Vision, Vol. 9, No. 1, pp. 13-24.
  11. Deepak Kumar Panda, and Sukadev Meher, 2016, “Detectionof Moving Objects Using Fuzzy Color Difference Histogram Based Background Subtraction”, IEEE SIGNAL PROCE-SSING LETTERS, Vol. 23, No. 1, pp. 45-49.
  12. Zhihu Wang, Kai Liao, Jiulong Xiong, and Qi Zhang, 2014 “Moving Object Detection Based on Temporal Information”, IEEE SIGNAL PROCESSING LETTERS, Vol.21, No. 11, pp. 1403-1407.


Moving Objects Detection, processing Time, percentage correct classification (PCC), Detection Accuracy, Conveyer Belt, Robot Arm, and Camera