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Ramar, K.
- Block-Based Tracking With Two Way Search
Abstract Views :158 |
PDF Views:0
Authors
J. Shajeena
1,
K. Ramar
2
Affiliations
1 Department of Computer Science and Engineering, James College of Engineering and Technology, IN
2 Einstein College of Engineering, IN
1 Department of Computer Science and Engineering, James College of Engineering and Technology, IN
2 Einstein College of Engineering, IN
Source
ICTACT Journal on Image and Video Processing, Vol 5, No 2 (2014), Pagination: 937-943Abstract
Tracking is essentially a matching problem. This paper proposes a tracking scheme for video objects on compressed domain. This method mainly focuses on locating the object region and evolving the detection of movement, which improves tracking precision. Motion Vectors (MVs) are used for block matching. At each frame, the decision of whether a particular block belongs to the object being tracked is made with the help of histogram matching. During the process of matching and evolving the direction of movement, similarities of target region are compared to ensure that there is no overlapping and tracking performed in a right way. Experiments using the proposed tracker on videos demonstrate that the method can reliably locate the object of interest effectively.Keywords
Motion Vector, Euclidean Distance, Histogram, Block Matching, DCT.
- Object Tracking with Rotation-Invariant Largest Difference Indexed Local Ternary Pattern
Abstract Views :219 |
PDF Views:2
Authors
J. Shajeena
1,
K. Ramar
2
Affiliations
1 Department of Computer Science and Engineering, James College of Engineering and Technology, IN
2 Einstein College of Engineering, IN
1 Department of Computer Science and Engineering, James College of Engineering and Technology, IN
2 Einstein College of Engineering, IN
Source
ICTACT Journal on Image and Video Processing, Vol 7, No 3 (2017), Pagination: 1408-1414Abstract
This paper presents an ideal method for object tracking directly in the compressed domain in video sequences. An enhanced rotation-invariant image operator called Largest Difference Indexed Local Ternary Pattern (LDILTP) has been proposed. The Local Ternary Pattern which worked very well in texture classification and face recognition is now extended for rotation invariant object tracking. Histogramming the LTP code makes the descriptor resistant to translation. The histogram intersection is used to find the similarity measure. This method is robust to noise and retain contrast details. The proposed scheme has been verified on various datasets and shows a commendable performance.Keywords
LTP, Motion Vector, Rotation-Invariant, Histogram, Object Tracking, DCT.References
- J. Shajeena and K. Ramar, “Block-Based Tracking with Two Way Search”, ICTACT Journal on Image and Video Processing, Vol. 5, No. 2, pp. 937-943, 2014.
- J. Shajeena and K. Ramar, “A Novel Way of Tracking Moving Objects in Video Scenes”, Proceedings of International Conference on Emerging Trends in Electrical and Computer Technology, pp. 805-810, 2011.
- J. Shajeena and K. Ramar, “Object Tracking in Videos: Methodologies and Measures”, International Journal of Applied Engineering Research, Vol. 10, No. 70, pp. 103110, 2015.
- R. Venkatesh Babu and Anamitra Makur, “Object-based Surveillance Video Compression using Foreground Motion Compensation”, Proceedings of 9th International Conference on Control, Automation, Robotics and Vision, 2006.
- Zhenhua Guo, Lei Zhang and David Zhang, “A Completed Modeling of Local Binary Pattern Operator for Texture Classification”, IEEE Transactions on Image Processing, Vol. 19, No. 6, pp. 1657-1663, 2010.
- Taha H. Rassem and Bee Ee Khoo, “Completed Local Ternary Pattern for Rotation Invariant Texture Classification”, The Scientific World Journal, Vol. 2014, pp. 1-10, 2014.
- Anuradha, Koneru and Manoj Kumar Tyagi, “A Novel Method of Face Recognition using Lbp, Ltp and Gabor Features”, International Journal of Scientific & Technology Research, Vol. 1, No. 5, pp. 31-35, 2012.
- Anaghav Malkapurkar, Rupali Patil and Sachin Murarka, “A New Technique for LBP Method to Improve Face Recognition”, International Journal of Emerging Technology and Advanced Engineering, Vol. 1, No. 1, pp. 67-71, 2011.
- A.A. Mohamed and R.V. Yampolskiy, “Adaptive Extended Local Ternary Pattern (AELTP) for Recognizing Avatar Faces”, Proceedings of International Conference on Machine Learning and Applications, Vol. 1, pp. 57-62, 2012.
- Amit Satpathy, Xudong Jiang and How-Lung Eng, “LBPbased Edge-Texture Features for Object Recognition”, IEEE Transactions on Image Processing, Vol. 23, No. 5, pp. 19531964, 2014.
- H. Rami, M. Hamri and Lh. Masmoudi, “Objects Tracking in Images Sequence using Local Binary Pattern (LBP)”, International Journal of Computer Applications, Vol. 63, No. 20, pp. 19-23, 2013.
- Sk. Md. Masudul Ahsan, J.K. Tan, H. Kim and S. Ishikawa, “Spatiotemporal LBP and Shape Feature for Human Activity Representation and Recognition”, International Journal of Innovative Computing, Information and Control, Vol. 12, No. 1, pp. 1-13, 2016.
- A. Anbarasa Pandian, and R. Balasubramanian, “Performance Analysis of Texture Image Retrieval for Curvelet, Contourlet Transform and Local Ternary Pattern Using MRI Brain Tumor Image”, International Journal in Foundations of Computer Science & Technology, Vol. 5, No. 6, pp. 33-46, 2015.
- Abdallah A. Mohamed, and Roman V. Yampolskiy, “Adaptive Extended Local Ternary Pattern (AELTP) for Recognizing Avatar Faces”, Proceedings of 11th International Conference on Machine Learning and Applications, Vol. 1, pp. 57-62, 2012.
- Jianfeng Ren, Xudong Jiang and Junsong Yuan, “Relaxed Local Ternary Pattern for Face Recognition”, Proceedings of 20th IEEE International Conference on Image Processing, pp. 3680-3684, 2013.
- Zhenhua Guo, Lei Zhang and David Zhang, “Rotation Invariant Texture Classification using LBP Variance (LBPV) with Global Matching”, Pattern Recognition, Vol.43, No. 3, pp. 706-719, 2010.
- Guoying Zhao, Timo Ahonen, Jiří Matas and Matti Pietikäinen, “Rotation-Invariant Image and Video Description with Local Binary Pattern Features”, IEEE Transactions on Image Processing, Vol. 21, No. 4, pp. 14651477, 2012.
- P. Tamije Selvy, T. Renuka Devi, R. Siva Keerthini and S. Umamaheswari, “A Proficient Extreme Learning Machine Approach for Tracking and Estimating Human Poses”, International Journal of Engineering and Computer Science, Vol. 4, No. 3, pp. 10908-10913, 2015.
- Sayed Hossein Khatoonabadi, and Ivan V. Bajić, “Video Object Tracking in the Compressed Domain using SpatioTemporal Markov Random Fields”, IEEE Transactions on Image Processing, Vol. 22, No. 1, pp. 300-313, 2013.