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Moving Object Detection using Contour

Shilpee A. Dave, Dr. M.S. Nagmode, Aditi Jahagirdar

Abstract


Computer Vision is the field which is growing very fast and rapidly. The main purpose of the object detection in video surveillance system is for safety of the human. Objects like human and vehicle have been important research area in Intelligent Transport System (ITS). Here in this paper, we have considered human and vehicles as an object. Basically, Contour-based approach is used to get moving object’s region. In this approach, consecutive frames are subtracted to get foreground moving objects and connected component of the contour is used for detecting the object. Here we have set the Region of Interest to extract detected object. We have used static camera for detection purpose. Different video database of video surveillance area is considered. Thresholding, binary image conversion, morphological operations are implemented for better detection of foreground objects. Experimental results are shown to elaborate the contour-based object detection method.

Keywords


Object Detection, Contour Detection, Region of Interest.

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References


M. Fatih Talu, Ibrahim Türkog˘lu, Mehmet Cebeci, ―A hybrid tracking method for scaled and oriented objects in crowded scenes‖, Expert Systems with Applications 38(2011), 2011 Elsevier Ltd., pp. 13682-13687.

Xin Zhang, Yuehua Gao, Xiaotao Wang, Jianing Li, Bing Wang, ―A Method for Detecting Pedestrians in Video Surveillance Scenes‖, 2012 International Conference on Systems and Informatics (ICSAI 2012), May 2012, pp. 2016-2019.

Zhengqiang Jiang, Du Q. Huynh, William Morany, Subhash Challay and Nick Spadaccini, ―Multiple Pedestrian Tracking using Colour and Motion Models‖, 2010 International conference on Digital image computing: Techniques and applications(DICTA), Dec 2010, pp.- 328-334.

Jin Zheng, Wan Zhang, Bo Li, ―Pedestrian detection based on background modeling and Head-shoulder-recognition‖, Proceedings of the 2012 International Conference on Wavelet Analysis and Pattern Recognition, Xian, pp. 227-232, 15-17 July, 2012.

Huibin Wang, Rong Lu, Xuewen Wu, Lili Zhang, Jie Shen, ―Pedestrian Detection and Tracking Algorithm Design in Transportation Video Monitoring System‖, 2009 IEEE International Conference on Information Technology and Computer Science, July 2009, pp 53-56.

Christoph G. Keller, Thao Dang, Hans Fritz, Armin Joos, Clemens Rabe, and Dariu M. Gavrila, ―Active Pedestrian Safety by Automatic Braking and Evasive Steering‖, IEEE Transaction on Intelligent Transportation Systems, Vol. 12, NO. 4, pp. 1292-1304, December 2011.

Paulo Vinicius Koerich, Borges, ―Pedestrian Detection Based on Blob Motion Statistics‖, 2013 IEEE Transactions on Circuits and Systems for Video Technology, Feb 2013, pp. 1-12.

Weina Ge, Robert T. Collins, R. Barry Ruback, ―Vision-Based Analysis of Small Groups in Pedestrian Crowds‖, IEEE Transactions on Pattern Analysis and Machine Intelligence, Vol. 34, No. 5, May 2012, pp. 1003-1016.

Thombre D.V., Nirmal J H, Lekha, Das, ―Human Detection and Tracking using Image Segmentation and Kalman Filter‖, 2009 IEEE International Conference on Intelligent Agent and Mult-Agent systems (IAMA), July 2009, pp. 1-5.

Ignacio Parra Alonso, David Fernandez Llorca, Miguel Angel Sotelo, Luis M. Bergasa, Pedro Revenga de Toro, Jess Nueuvo, Manuel Ocana, and Miguel Angel Garcia Garrido, ―Combination of Feature Extraction Methods for SVM Pedestrian Detection‖, IEEE Transactions on

Intelligent Transportation Systems, Vol. 8, NO. 2, June 2007, pp. 292-307.

Ruolin Zhang, Jian Ding, ―Object Tracking and Detecting Based on Adaptive Background Subtraction‖, 2012 International Workshop on Information and Electronics Engineering (IWIEE), Engineering 29 (2012) 1351–1355.

Gonzalez, ―Digital Image Processing‖, Pearson Education India, 2009.

―Sensor Fusion-Based Pedestrian Collision Warning System with Crosswalk Detection‖, S. Suzuki, P. Raksincharoensak, I. Shimizu, M. Nagai, R. Adomat, 2010 IEEE Conference on Intelligent Vehicles Symposium (IV), June 2010, pp. 355-360.

http://www.dai.ed.ac.uk/homes/rbf/CAVIAR/

Zhaoxia Fu, Yan Han, ―Centroid weighted Kalman filter for visual object tracking‖, Elsevier Journal of Measurement 45(2012), pp. 650-655.

Gary Bradski and Adrian Kaehler, ―Learning OpenCV Computer Vision with the OpenCV Library‖, O’Reilly,2008.

Amedome Min-Dianey Kodjo, Yang Jinhua2, ―Real-time Moving Object Tracking in Video‖, IEEE 2012 International Conference on Optoelectronics and Microelectronics (ICOM), pp. 580-584.

Mr. Brojeshwar Bhowmick, Mr. Sambit Bhadra, Mr. Arijit Sinharay, ―Stereo Vision Based Pedestrians Detection and Distance Measurement for Automotive Application, IEEE 2011 Second International Conference on Intelligent Systems, Modelling and Simulation, pp. 25-29.

Milan Sonka, Vaclav Hlavac, Roger Boyle, ―Digital Image Processing and Computer Vision‖, Cengage Learning, India, 2008.

D. Comaniciu, V. Ramesh, and P. Meer, ―Kernel-Based Object Tracking‖. IEEE Transactions on Pattern Analysis and Machine Intelligence, Vol. 25, No. 5, 2003, pp.564-577.

Weiming Hu, Tieniu Tan, Liang Wang, and Steve Maybank, ―A Survey on Visual Surveillance of Object Motion and Behaviors‖, IEEE Transactions ON Systems, Man, and Cybernetics—Part C: Applications And Reviews, Vol. 34, NO. 3, August 2004, pp. 334-353.

Shilpee A. Dave, Dr. M.S. Nagmode, Aditi Jahagirdar, ―Statistical Survey on Object Detection and tracking Methodologies‖, International Journal of Scientific & Engineering Research Volume 4, Issue3, March-2013, pp. 1-8.

http://en.wikipedia.org/wiki/Background_subtractin.

http://en.wikipedia.org/wiki/Mathematical_morphology.


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