An Improved Algorithm for Moving Object Tracking based on Frame and Edge Differences

M. F. Sarwar, A. Basit, H. Z. Ahmad


Object tracking is gaining interest of researchers in the field of image processing and computer vision. Many methods have been proposed by the researchers in this field. In this paper, an improved algorithm for moving object tracking is proposed based on the frame difference and edge difference methods. Canny edge detector is applied to detect edges of current and previous frames and to get the difference of both edge images. Afterwards, the simple frame difference method is applied on both frames then the result is combined with the resulting image obtained after edge difference. Improved Otsu method is used to threshold the image and morphological filtering is applied to remove noise. Subsequently connectivity analysis is carried out to obtain the moving objects. This algorithm takes advantage of both frame difference and edge difference methods to improve the accuracy in detecting the moving objects. Experiments are performed on various videos which show efficient results in very short time.

Full Text:



F. M. Porikli, "Real-time Video Object Segmentation for MPEGEncoded Video Sequences", SPIE Conference on Real-Time

Imagining VIII, vol. 5297, pp. 195-203, 2004.

J. Nascimento and J. Marques, " Performance evaluation of object

detection algorithms for video surveillance ", IEEE Transactions

on Multimedia, vol. 8, pp. 761-774, (2006).

C. Kim and J.-N. Hwang, IEEE Transactions on Circuits and

Systems for Video Technology, vol. 12, pp. 122-129, 2002.

G. L. Foresti, "Object recognition and tracking for remote video

surveillance", IEEE Transactions on Circuits and Systems for

Video Technology, vol. 9, pp. 1045-1062, 1999.

R. Wang, F. Bunyak, G. Seetharaman and K. Palaniappan, Static

and Moving Object Detection Using Flux Tensor with Split

Gaussian Models, presented at Computer Vision and Pattern

Recognition Workshops (CVPRW), IEEE Conference on, (2014).

X. Zhou, C. Yang and W. Yu, IEEE Transactions on Pattern

Analysis and Machine Intelligence, vol. 35, pp. 597-610, 2013.

A. Yilmaz, O. Javed, and M. Shah, Object Tracking : A Survey,

ACM Computing Surveys, vol. 38, pp. 1-45, 2006.

C. Stauffer and W. E. L. Grimson, "Adaptive Background Mixture

Models for Real-time Tracking", IEEE Computer Society

Conference on Computer Vision and Pattern Recognition (1999).

Z. Sun, S.-a. Zhu and D. Zhang, "Real-Time and Automatic

Segmentation Technique for Multiple Moving Objects in Video

Sequence", IEEE International Conference on Control and

Automation, pp. 825 - 829, 2007.

M. Weng, G. Huang and X. Da, "A New Interframe Difference

Algorithm for Moving Target Detection", 3rd International

Congress on Image and Signal Processing, pp. 285 - 289, 2010.

Z. Chaohui, D. Xiaohui, X. Shuoyu, S. Zheng, and L. Min, "An

Improved Moving Object Detection Algorithm Based on Frame

Difference and Edge Detection", 4th International Conference on

Image and Graphics, pp. 519 - 523, 2007.


  • There are currently no refbacks.