Dual Tree Complex Wavelet Transform Based Video Object Tracking

Srivastava

2015

Multimed Tools Appl

Self Cite

Moving object tracking is one of the challenging problems in computer vision and it has numerous applications in surveillance system, traffic monitoring etc. The goal of object tracking algorithm is to locate a moving object in consecutive video frames. Tracking of moving object in a video becomes difficult due to random motion of objects. This paper introduces a new algorithm for moving object tracking by exploiting the properties of Daubechies complex wavelet transform and Zernike moment. The proposed method uses combination of Daubechies complex wavelet transform and Zernike moment as a feature of object. The motivation behind using combination of these two as a feature of object, because shift invariance and better edge representation property make Daubechies complex wavelet transform suitable for locating object in consecutive frames whereas rotation invariance properties of Zernike moment is also helpful for correct object identification in consecutive frames. Therefore combination of these two as feature of object gives better results. The proposed method matches Zernike moments of Daubechies complex wavelet coefficients of object in the first frame to next consecutive frames. The experimental results and performance evaluation parameters validate that the proposed method gives better performance against other state-of-the-art methods.

Section: Literature Reviewmentioning

confidence: 98%

Section: Literature Reviewmentioning

confidence: 99%

Object tracking using combination of daubechies complex wavelet transform and Zernike moment

Srivastava

2015

Multimed Tools Appl

Self Cite

“…Detecting moving regions provide a base for later process such as object tracking etc. [3][4][5] because only these segmented regions need to be considered in the later processes. In spite of more than four decades of research in the area of image segmentation, very few work have been done on motion segmentation, due to lack of unifying theories.…”

Section: Introductionmentioning

confidence: 99%

Curvelet transform based moving object segmentation

Srivastava

2013 IEEE International Conference on Image Processing

et al. 2013

Self Cite

In this paper, we have proposed a new method for segmentation of moving objects, which is based on single change detection applied on curvelet coefficients of two consecutive frames. The wavelet transform is widely used in moving object segmentation but it can not describe curve discontinuities. Therefore we have used curvelet transform for segmentation of moving objects. The proposed method is simple and does not require any other parameter except curvelet coefficients. Results after applying the proposed method for segmentation of moving object are compared with other state-of-the-art methods in terms of visual as well as quantitative performance measures viz. Misclassification penalty, Relative position based measure and Structural content. The proposed method is found to be better than other methods.

“…In transform domain processing, wavelet transform is very promising tool for object tracking purpose. Several methods [10][11][12][13] exists for object tracking using wavelet transform. However, the wavelet transform does not handle curve discontinuity, and discontinuities across a single curve affect all wavelet coefficients at the curve.…”

Section: Introductionmentioning

confidence: 99%

Contourlet transform based human object tracking

et al. 2015

Self Cite

Human object tracking is a challenging problem in video processing applications, and is an important step toward development of surveillance system. In this paper, we have proposed a new method for tracking of a human object in video sequence which is based on Contourlet transform. We have chosen Contourlet transform as it has high directionality and represents salient features of image such as edges, curves and contours in better way as compared with wavelet transform. The proposed method is simple and does not require any other parameter except Contourlet coefficients. Results after applying the proposed method for human object tracking is compared with other state-of-theart methods in terms of visual as well as quantitative performance measures viz. Euclidean distance and Mahalanobis distance. The proposed method is found to be better than other methods.