Multitextured Segmentation for Improving Moving Objects Detection and Tracking
Image segmentation based on multiple texture features has significant issues in the areas of content based image extraction, image outline recognition, medical image processing, remote sensing, image segmentation through pattern identification and monitoring in crowded public places. Active contour color recognition methods were developed for detecting and tracking object in sequential images. However, the presence of dynamic shadows was a critical issue in foreground segmentation. Therefore, Multi Textured-based Object Segmentation (MTOS) technique is proposed in this study for improving the detection and tracking of moving objects. The proposed technique first locates the objects and boundaries of images with the same label distributed with certain visual characteristics. Next, preprocessing technique is performed using median filtering to reduce the distortion and noise in video frames. Then, texture-based segmentation is carried out using an adaptive threshold-based approach to avoid distortions while detecting moving objects. Detecting moving regions is accomplished by comparing the current video frame from a reference background in a pixel-by-pixel manner with multiple texture features. The effectiveness of moving object image segmentation through texture features is evaluated. The experimental results show that our proposed technique performs better in terms of segmentation accuracy, segmentation time, peak signal to noise ratio and object detection rate.
A. S. Silva, F. M. Q. Severgnini, M. L. Oliveira, V. M. S. Mendes and Z. M. A. Peixoto, “Object Tracking by Color and Active Contour Models Segmentation”, IEEE Latin America Transactions, Volume 14, Issue 3, March 2016, Pages 1488 – 1493.
Pojala Chiranjeevi and Somnath Sengupta, “Neighborhood Supported Model Level Fuzzy Aggregation for Moving Object Segmentation”, IEEE Transactions on Image Processing, Volume 23, Issue 2, February 2014, Pages 645 – 657.
Ms Jyoti J. Jadhav, “Moving Object Detection and Tracking for Video Survelliance”, International Journal of Engineering Research and General Science Volume 2, Issue 4, July 2014, Pages 372-378.
Sukanyathara J and Alphonsa Kuriakose, “An Optimized Framework for Detection and Tracking of Video Objects in Challenging Backgrounds”, the International Journal of Multimedia & Its Applications (IJMA), Volume 6, Issue 4, August 2014, Pages 27 – 38
Zhengyang Wu, Fuxin Li, Rahul Sukthankar and James M. Rehg, “Robust Video Segment Proposals with Painless Occlusion Handling”, The IEEE Conference on Computer Vision and Pattern Recognition (CVPR), Computer Vision Foundation, 2015, Pages 4194 – 4203
Shih-Wei Sun, Yu-Chiang Frank Wang , Fay Huang , Hong-Yuan Mark Liao, “Moving foreground object detection via robust SIFT trajectories”, Journal of Visual Communication and Image Representation, Elsevier, Volume 24, Issue 3, April 2013, Pages 232–243
Jianping Han, Tian Tan, Longfei Chen and Daxing Zhang, “Moving Objects Representation for Object Based Surveillance Video Retrieval System”, International Journal of Security and Its Applications, Volume 8, Issue 2, March 2014, Pages 315 – 322
Shao-Yi Chien, Wei-Kai Chan, Yu-Hsiang Tseng, and Hong-Yuh Chen, “Video Object Segmentation and Tracking Framework with Improved Threshold Decision and Diffusion Distance”, IEEE Transactions on Circuits and Systems for Video Technology, Volume 23, Issue 6, JUNE 2013, Pages 921- 934
Ivan Huerta, Michael B. Holte, Thomas B. Moeslund and Jordi Gonz`alez, “Chromatic Shadows Detection and Tracking for Moving Foreground Segmentation”, Image and Vision Computing, Volume 41, September 2015, Pages 42-53.
Manya V. Afonso, Jacinto C. Nascimento and Jorge S. Marques, “Automatic Estimation of Multiple Motion Fields From Video Sequences Using a Region Matching Based Approach”, IEEE Transactions on Multimedia, Volume 16, Issue 1, January 2014, Pages 1-14.
Mansi Saraswat, Anil Kumar Goswami, Aastha Tiwari, “Object Recognition Using Texture Based Analysis”, International Journal of Computer Science and Information Technologies (IJCSIT), Volume 4, Issue 6 , 2013, Pages 775-782
Yanli Wan, Xifu Wang, and Hongpu Hu, “Automatic Moving Object Segmentation for Freely Moving Cameras”, Mathematical Problems in Engineering, Hindawi publishing corporation , Volume 2014, August 2014, Pages 1-11
Xiaowei Zhou, Can Yang, and Weichuan Yu, “ Moving object detection by detecting contiguous outliers in the low-rank representation,” IEEE Transactions on Pattern Analysis and Machine Intelligence, volume 35, Issue 3, 2013, Pages 597–610
Yi Deng, Philip Coen, Mingzhai Sun, Joshua W. Shaevitz, “ Efficient Multiple Object Tracking Using Mutually Repulsive Active Membranes”, PLoS ONE journal, Volume 8, Issue 6, 2013, Pages 1-11
Hamid Izadinia, Imran Saleemi, and Mubarak Shah, “Multimodal Analysis for Identification and Segmentation of Moving-Sounding Objects”, IEEE Transactions on Multimedia, Volume 15, Issue 2, February 2013, Pages 378-390
Zhengzheng Tu, Andrew Abel, Lei Zhang, Bin Luo, Amir Hussain, “A New Spatio-Temporal Saliency-Based Video Object Segmentation”, Cognitive Computation, Springer, Volume 8, Issue 4, August 2016, Pages 629–647
Shubhangi Vaikole, Sudhir D.Sawarkar, “Segmentation of Moving Object with Uncovered Background, Temporary Poses and GMOB” Procedia Computer Science, Elsevier, Volume 79, 2016, Pages 299 – 304
Shunli Zhang, Xin Yu, Yao Sui, Sicong Zhao, and Li Zhang, “Object Tracking with Multi-View Support Vector Machines”, IEEE Transactions on Multimedia, Volume 17, Issue 3, March 2015, Pages 265 - 278
Mrinali M. Bhajibhakare, Pradeep K. Deshmukh, “To Detect and Track Moving Object for Surveillance System”, International Journal of Innovative Research in Computer and Communication Engineering, Volume 1, Issue 4, June 2013, Pages 945-949
Fuyuan Xu, Guohua Gu, Kan Ren and Weixian Qian, “Motion Segmentation by New Three-View Constraint from a Moving Camera”, Hindawi Publishing Corporation, Mathematical Problems in Engineering, Volume 2015, January 2015 , Pages 1-14.
- There are currently no refbacks.
This work is licensed under a Creative Commons Attribution 3.0 License.