Optimal Object Detection and Tracking Using Improved Particle Swarm Optimization (IPSO)

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In the current years, Object detection and tracking from the video sequence has turn out to be an exciting area of research. Using a single feature this research suggests new object exposure and tracking plan where video segmentation, characteristic extraction, feature clustering and object recognition are shared easily. The database video clips are partitioned into different shots, proceeding to implement the characteristic extraction. Feature extraction and tracking of the same video clips for the particular query clips are the two stages the organization is included. Mainly the contour of the frame can be examined by means of Enhanced Level Set algorithm. From the major contour detected the characteristics such as color, texture, edge density and motion are uttered. Obtained from parallel measures in the characteristic extraction, initially the motion feature is extorted by means of a competent motion estimation algorithm. As a result for tracking process here we employ Improved Particle Swarm Optimization (IPSO). For making sure high tracking presentation in the end, the tracked frames are collected by using a competent clustering algorithm. The expected strategy will be executed in MATLAB by means of a range of video clips and planned to be assessed. The appearance of the anticipated system will be considered by precision and recall measure
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Improved Particle Swarm Optimization (IPSO); Object Detection

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Barron, J., Fleet, D., and Beauchemin, S. 1994. Performance of optical flow techniques. International Journal of Computer Vision Vol. 12, No. 4, pp: 43–77, 1994.

Xing, Junliang, Haizhou Ai, and Shihong Lao. "Multi-object tracking through occlusions by local tracklets filtering and global tracklets association with detection responses." In Computer Vision and Pattern Recognition. pp: 1200-1207, 2009.

Okuma, K., Taleghani, A., De Freitas, N., Little, J. J., & Lowe, D. G. A boosted particle filter: Multitarget detection and tracking. In Computer Vision-ECCV, pp: 28-39, 2004

Friedman, J., Hastie, T., and Tibshirani, R. Additive logistic regression: A statistical view of boosting annals of statistics. Vol. 2, pp: 337–374, 2000.

Rasmussen, C. and Hager, G. Probabilistic data association methods for tracking complex visual objects. Vol. 23, No. 6, pp: 560–576, 2001.

Wren, C., Azarbayejani, A., And Pentland, A. Real-time tracking of the human body. IEEE Trans. Patt. Analy. Mach. Intell., pp: 780–785, 1997.

Kim, C., & Hwang, J. N. Fast and automatic video object segmentation and tracking for content-based applications. Circuits and Systems for Video Technology, Vol. 12, No. 2, pp: 122-129, 2002.

Mittal, A. and Davis, L. M2 tracker: A multiview approach to segmenting and tracking people in a cluttered scene. Vol 3, pp: 189–203, 2003.

Jepson, A., Fleet, D., And Elmaraghi, T. Robust online appearance models for visual tracking. IEEE Trans. Patt. Analy. Mach. Intell. Vol. 25, No. 10, pp: 1296–1311, 2003.

Paragios, N., & Deriche, R. Geodesic active contours and level sets for the detection and tracking of moving objects. Pattern Analysis and Machine Intelligence, Vol. 3, pp: 266-280, 2000.

Vu, T. D., Burlet, J., & Aycard. Grid-based localization and local mapping with moving object detection and tracking. Information Fusion, Vol. 12, No. 1, pp: 58-69, 2007.

Hsia, C. H., & Guo, J. Ming. Efficient modified directional lifting-based discrete wavelet transform for moving object detection. Signal Processing, pp: 138-152.

Cuevas, C., & García. Improved background modeling for real-time spatio-temporal non-parametric moving object detection strategies. Image and Vision Computing. 2013.

Karasulu, Bahadir, and Serdar Korukoglu. "Moving object detection and tracking by using annealed background subtraction method in videos: Performance optimization." Expert Systems with Applications, Vol. 1, pp: 33-43, 2012.

Chieh-Chih Wang and Chuck Thorpe, "Simultaneous Localization and Mapping with Detection and Tracking of Moving Objects" In.Proc.of the IEEE International Conference on Robotics and Automation (ICRA), 2002.

S. Kirkpatrick,C. D. Gelatt and M. P. Vecchi, "Optimization by Simulated Annealing Science, New Series, Vol. 220, No. 4598,pp. 671-680,1985

Zhaozheng Yin and Robert T. Collins, ”Object Tracking and Detection after Occlusion via Numerical Hybrid Local and Global Mode-seeking” , IEEE Computer Vision and Pattern Recognition (CVPR'08), Anchorage, Alaska, 2008.

Leibe, B.; Schindler, K.; Van Gool, L., "Coupled Detection and Trajectory Estimation for Multi-Object TrackingComputer Vision, 2007. ICCV 2007. IEEE 11th International Conference on, 14-21 Oct. 2007, pp.1-8.

J. Fernandeza, R. Guerreroa, N. Mirandaa and F. Piccolia ,"Multi-Level Paralelism In Image Identification,"Mecanica Computational,Vol.28,pp.227-240,Argentina,Nov 2009.

Kalpesh R Jadav, Prof.M.A.Lokhandwala and Prof.A.P.Gharge ,"Vision based moving object detection and tracking", National Conference on Recent Trends in Engineering & Technology, May 2011.

Qiu, Y., Liu, C., A collaborative task allocation mechanism for wireless sensor networks, (2012) International Review on Computers and Software (IRECOS), 7 (7), pp. 3821-3825.

Ma, Z., Wang, Y., Zheng, Y., Zou, X., An improved segmentation method based on Semi-Fuzzy cluster, (2012) International Review on Computers and Software (IRECOS), 7 (7), pp. 3452-345.


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