Refine your search
Collections
Co-Authors
A B C D E F G H I J K L M N O P Q R S T U V W X Y Z All
Balakrishnan, G.
- A Hybrid Approach to Human Skin Region Detection
Abstract Views :290 |
PDF Views:0
Authors
Affiliations
1 Department of Computer Applications, J.J. College of Engineering & Technology, Tamil Nadu, IN
2 Department of Computer Science and Engineering, Indra Ganesan College of Engineering, Tamil Nadu, IN
1 Department of Computer Applications, J.J. College of Engineering & Technology, Tamil Nadu, IN
2 Department of Computer Science and Engineering, Indra Ganesan College of Engineering, Tamil Nadu, IN
Source
ICTACT Journal on Image and Video Processing, Vol 1, No 3 (2011), Pagination: 143-149Abstract
Face recognition is important in research areas like machine vision and complex security systems. Skin region detection is a vital factor for processing in such systems. Hence the proposed paper focuses on isolating the regions of an image corresponding to human skin region through the hybrid method. This paper intends to combine the skin region detected from RGB and YCbCr color spaces image by the explicit skin color conditions and the skin label cluster identified from CIELab color space image, which is clustered by Hillclimbing segmentation with K-Means clustering algorithm. Then the resultant image is dilated by arbitrary shape and filtered by the median filter, in order to enhance the skin region and to avoid the noise respectively. The proposed method has been tested on various real images, which contain one or more human beings and the performance of skin region detection is found to be quite satisfactory.Keywords
Color Spaces, Dilation, HillClimbing Segmentation with K-Means, Median Filter.
- Multiple Target Tracking Using Cost Minimization Techniques
Abstract Views :198 |
PDF Views:3
Authors
Affiliations
1 Department of Computer Applications, Pavendar Bharathidasan College of Engineering and Technology, IN
2 Department of Computer Science and Engineering, Indra Ganesan College of Engineering, IN
1 Department of Computer Applications, Pavendar Bharathidasan College of Engineering and Technology, IN
2 Department of Computer Science and Engineering, Indra Ganesan College of Engineering, IN
Source
ICTACT Journal on Image and Video Processing, Vol 7, No 3 (2017), Pagination: 1424-1432Abstract
Many applications such as intelligent transportation, video surveillance, robotics of computer vision mainly depend on task of multiple target tracking. It consists of process of detection, classifications and tracking. In this novel approach of multi target tracking, cost terms are formulated to attain the global optimization which includes the entire representation of the issues such as target tracking, operational representation, collision handling and trajectory processing. Furthermore, two optimization strategies such as the gradient descent which is performed on multiple feature space to obtain local minima of a density function from the given sample of data and gradient ascent which is carried out to achieve a likelihood matching of the target and used to handle the partial evidence of the image, and also uncertainty of the various targets are minimized. . In this study, the proposed works are tested on different publicly available datasets using the metric evaluation and also compared with the various methods based on issues of target tracking. This study will also provide a better understanding of the problem, knowledge of the methods, and experimental evaluation skill for further research works.Keywords
Multiple Target Tracking, Surviellance, Cost Minimization, Optimization, Tracking Metrics.References
- M. Everingham et.al., “The Pascal Visual Object Classes Challenge”, Proceedings of 1st Pascal Machine Learning Challenges Workshop, pp. 117-176, 2012.
- P. Dollar, C. Wojek, B. Schiele and P. Perona, “Pedestrian Detection: A Benchmark”, Proceedings of IEEE Conference on Computer Vision and Pattern Recognition, pp. 304-311, 2009.
- S.M. Seitz, B. Curless, J. Diebel, D. Scharstein and R. Szeliski, “A Comparison and Evaluation of Multi-View Stereo Reconstruction Algorithms”, Proceedings of IEEE Computer Society Conference on Computer Vision and Pattern Recognition, pp. 519-528, 2006.
- S. Baker, D. Scharstein, J.P. Lewis, S. Roth, M.J. Black, and R. Szeliski. “A Database and Evaluation Methodology for Optical Flow”, International Journal of Computer Vision, Vol. 92, No. 1, pp. 1-31, 2011.
- Andreas Geiger, Philip Lenz and Raquel Urtasun, “Are We Ready for Autonomous Driving? The KITTI Vision Benchmark Suite”, Proceedings of IEEE Conference on Computer Vision and Pattern Recognition, pp. 3354-3361, 2012.
- J. Black, T. Ellis and P. Rosin, “Multi View Image Surveillance and Tracking”, Proceedings of Workshop on Motion and Video Computing, pp.1-6, 2002.
- D. Reid. “An Algorithm for Tracking Multiple Targets”, IEEE Transactions on Automatic Control, Vol. 24, No. 6, pp. 843-854, 1979.
- K. Okuma, A. Taleghani, O.D. Freitas, J.J. Little and D.G. Lowe, “A Boosted Particle Filter: Multitarget Detection and Tracking”, Proceedings of 8th European Conference on Computer Vision, Vol. 1, pp. 28-39, 2004.
- J. Vermaak, A. Doucet and P. Perez, “Maintaining Multi-Modality through Mixture Tracking”, Proceedings of 9th Conference on Computer Vision, pp. 1-7, 2003.
- T.E. Fortmann, Y. Bar-Shalom and M. Scheffe, “Multi-Target Tracking using Joint Probabilistic Data Association”, Proceedings of IEEE Conference on Decision and Control including the Symposium on Adaptive Processes, pp. 807-812, 1980.
- Mykhaylo Andriluka, Stefan Roth and Bernt Schiele, “Monocular 3D Pose Estimation and Tracking by Detection”, Proceedings of IEEE Conference on Computer Vision and Pattern Recognition, pp. 623-630, 2010.
- A. Andriyenko, S. Roth and K. Schindler, “An Analytical Formulation of Global Occlusion Reasoning for Multi-Target Tracking”, Proceedings of 11th International IEEE Workshop on Visual Surveillance, pp. 1839-1846, 2011.
- Anton Andriyenko and Konrad Schindler, “Globally Optimal Multi-Target Tracking on a Hexagonal Lattice”, Proceedings of 11t European Conference on Computer Vision, pp. 466-479, 2010.
- A. Andriyenko and K. Schindler, “Multi-Target Tracking by Continuous Energy Minimization”, Proceedings of IEEE Conference on Computer Vision and Pattern Recognition, pp. 1265-1272, 2011.
- B. Benfold and I. Reid, “Stable Multi-Target Tracking in Real-Time Surveillance Video”, Proceedings of IEEE Conference on Computer Vision and Pattern Recognition, pp. 2011.
- M. Kamaraj and Balakrishnan, “Surveillance of Human Tracking using Gaussian Beta-Likelihood Matching and Kalman Filter”, International Journal of Applied Engineering Research, Vol. 10, No. 14, pp 34375-34382, 2015
- M. Kamaraj and Balakrishnan, “Optimization of Multi-Target Tracking and Occlusion Handling using Mean Shift Method”, International Journal of Advanced Research in Computer science and Software Engineering, Vol. 5, No. 9, pp. 367-375, 2015.
- B. Leibe, K. Schindler and L. Van Gool, “Coupled Detection and Trajectory Estimation for Multi-Object Tracking”, Proceedings of 11t International Conference on Computer Vision, pp. 2-8, 2007.
- Hao Jiang, Sidney Fels and James J. Little, “A Linear Programming Approach for Multiple Object Tracking”, Proceedings of IEEE Conference on Computer Vision and Pattern Recognition, pp. 1-8, 2007.
- L. Zhang, Y. Li and R. Nevatia, “Global data association for multiobject tracking using network flows”, Proceedings of IEEE Conference on Computer Vision and Pattern Recognition, pp. 1-8, 2008.
- M. Rodriguez, I. Laptev, J. Sivic and J.Y. Audibert, “Density-Aware Person Detection and Tracking in Crowds”, Proceedings of IEEE International Conference on Computer Vision, pp. 2423-2430, 2011.
- Junliang Xing, Haizhou Ai and Shihong Lao, “Multi-Object Tracking through Occlusions by Local Tracklets Filtering and Global Tracklets Association with Detection Responses”, Proceedings of IEEE Conference on Computer Vision and Pattern Recognition, pp. 1200-1207, 2009.
- C. Wojek, S. Walk, S. Roth and B. Schiele, “Monocular 3D Scene Understanding with Explicit Occlusion Reasoning”, Proceedings of IEEE Conference on Computer Vision and Pattern Recognition, pp. 1993-2000, 2011.
- M.D. Breitenstein, F. Reichlin, B. Leibe, E. Koller-Meier and L. Van Gool, “Robust Tracking-by-Detection using a Detector Confidence Particle filter”, Proceedings of 12th International Conference on Computer Vision, pp. 1515-1522, 2009.
- A. Milan, S.Roth and K.Schindler, “Continuous Energy Minimization for Multi-Target Tracking”, IEEE Transactions on Pattern Analysis and Machine Intelligence, Vol. 36, No. 1, pp. 58-72, 2014.
- L. Kratz and K. Nishino, “Tracking with Local Spatio-Temporal Motion Patterns in Extremely Crowded Scenes”, Proceedings of IEEE Conference on Computer Vision and Pattern Recognition, pp. 693-700, 2010.
- W. Choi and S. Savarese, “Multiple Target Tracking in World Coordinate with Single, Minimally Calibrated Camera”, Proceedings on 11th European Conference on Computer Vision, pp. 553-567, 2010.
- Zhen Qin and Christian R. Shelton, “Improving Multi-Target Tracking via Social Grouping”, Proceedings of IEEE Conference on Computer Vision and Pattern Recognition, pp. 1972-1978, 2012.
- R.T. Collins, “Mean-Shift Blob Tracking through Scale Space”, Proceedings of IEEE Computer Society Conference on Computer Vision and Pattern Recognition, pp. 234-240, 2003.
- J. Ning, L. Zhang, D. Zhang and C. Wu, “Scale and Orientation Adaptive Mean Shift Tracking”, IET Computer Vision, Vol. 6, No. 1, pp. 52-61, 2012.
- Shou Zhang and Yaakov Bar-Shalom, “Robust Kernel-based Object Tracking with Multiple Kernel Centers”, Proceedings of 12th International Conference on Information Fusion, pp. 1014-1021, 2009.
- Alper Yilmaz, “Object Tracking by Asymmetric Kernel Mean Shift with Automatic Scale and Orientation Selection”, Proceedings of IEEE Conference on Computer Vision and Pattern Recognition, pp. 1-6, 2007.
- B.Z. De Villiers, W.A. Clarke and P.E. Robinson, “Mean shift Object Tracking with Occlusion Handling”, Available: http://www.prasa.org/proceedings/2012/prasa2012-36.pdf.
- Z. Zivkovic and B. Krose. “An EM-like Algorithm for Color-Histogram-based Object Tracking”, Proceedings of IEEE Computer Society Conference on Computer Vision and Pattern Recognition, pp. 1-6, 2004.
- Q.A. Nguyen, A. Robles-Kelly and C. Shen, “Kernel-based Tracking from a Probabilistic Viewpoint”, Proceedings of IEEE Computer Society Conference on Computer Vision and Pattern Recognition, pages 1-8, 2007.
- I. Guskov, “Kernel-based Template Alignment”, Proceedings of IEEE Conference on Computer Vision and Pattern Recognition, pp. 610-617, 2006.
- Cavira Test Case Scenario, Available: at: http://homepages.inf.ed.ac.uk/rbf/CAVIARDATA1/
- J.F. Henriques, R. Caseiro and J. Batista, “Globally Optimal Solution to Multi-Object Tracking with Merged Measurements”, Proceedings of IEEE Conference on Computer Vision, pp. 2470-2477, 2011.
- J. Berclaz, F. Fleuret, E. Turetken and P. Fua, “Multiple Object Tracking using K-Shortest Paths Optimization”, IEEE Transactions on Pattern Analysis and Machine Intelligence, Vol. 33, No. 9, pp. 1806-1819, 2011.
- PETS-Performance Evaluation of Tracking and Surveillance, Available at: http://www.cvg.reading.ac.uk/slides/pets.html
- B. Wu and R. Nevatia, “Tracking of Multiple, Partially Occluded Humans based on Static Body Part Detection”, Proceedings of IEEE Conference on Computer Vision and Pattern Recognition, pp. 1-8, 2006.