Email this article 
Open Access
Subscription or Fee Access
Survey Paper on Object Detection in Dynamic Background for Outdoor Surveillance System
Abstract
In current scenario, the need of surveillance systems are increasing continuously throughout the world. Video surveillance system is the monitoring of behavior, activities and other changing information for the purpose of impacting, managing, coordinating or security of people. The word surveillance comes from a French expression for “watching over”. Surveillance used by governments for intelligent gathering, prevention of crime, the assurances of procedure, person, group or object. It is likewise utilized by criminal associations to design and perpetrate violations, for example, theft and hijacking, by organizations to accumulate insight, and by private specialists. Outdoor surveillance have many parameter for security reason. Firstly, system have to deal with different atmospheric changes like rain, haze and fog and then detect object from the image. So, in security reason it is very hard to detect object in such type of dynamic background.
Keywords
References
Kumar, Punith, and P. S. Puttaswamy. "Moving text line detection and extraction in TV video frames." Advance Computing Conference (IACC), 2015 IEEE International. IEEE, 2015.
J. Clerk Maxwell, A Treatise on Electricity and Magnetism, 3rd ed., vol. 2. Oxford: Clarendon, 1892, pp.68–73.
Covell, Michele, Shumeet Baluja, and Michael Fink. "Advertisement detection and replacement using acoustic and visual repetition." 2006 IEEE Workshop on Multimedia Signal Processing. IEEE, 2006.
Jung, Cheolkon, and Licheng Jiao. "Korean-English bilingual videotext recognition for news headline generation based on a split-merge strategy." Journal of Real-Time Image Processing 11.1 (2016): 167-177.
Wang, Jinqiao, et al. "Automatic TV logo detection, tracking and removal in broadcast video." International Conference on Multimedia Modeling. Springer Berlin Heidelberg, 2007.
Kannao, Raghvendra, and Prithwijit Guha. "TV News Commercials Detection using Success based Locally Weighted Kernel Combination." arXiv preprint arXiv:1507.01209 (2015).
10Qi, Wei, et al. Integrating visual, audio and text analysis for news video. Image Processing, 2000. Proceedings. 2000 International Conference on. Vol. 3. IEEE, 2000.
4Kumar, Anubhav. An Efficient Approach for Text Extraction in Images and Video Frames Using Gabor Filter. International Journal of Computer and Electrical Engineering 6.4 (2014) 316.
Montgomery, Martin, and Debing Feng. "‘Coming up next’: The discourse of television news headlines." Discourse & Communication (2016): 1750481316659397.
Thai V. Hoang, S. Tabbone(2010),“Text Extraction From Graphical Document Images Using Sparse Representation”in Proc. Das, pp 143–150.
Sumathi, C. P., T. Santhanam, and G. Gayathri Devi. "A survey on various approaches of text extraction in images." International Journal of Computer Science and Engineering Survey 3.4 (2012): 27.
Davod Zaravi, Habib Rostami, Alireza Malahzaheh, S.S Mortazavi(2011),” Journals Subheadlines Text Extraction Using Wavelet Thresholding And New Projection Profile”, WorldAcademy Of Science, Engineering And Technology .Issue 73.
Syed Saqib Bukhari , Thomas M. Breuel,Faisal Shafait(2009), “Textline Information Extraction From Grayscale Camera-Captured Document Images “, ICIP Proceedings Of The 16th IEEE International Conference On Image Processing, pp: 2013 – 2016.
Kohei Arai1 , Herman Tolle(2011),” Text Extraction From Tv Commercial Using Blob Extraction Method”, International Journal Of Research And Reviews In Computer Science Vol. 2, No. 3
Wonder Alexandre Luz Alves And Ronaldo Fumio Hashimoto (2010),”Text Regions Extracted From Scene Images By Ultimate Attribute Opening And Decision Tree Classification”, Proceedings of the 23rd Sibgrapi Conference On Graphics, Patterns And Images.
Gllavata, Julinda, Ralph Ewerth, and Bernd Freisleben. "A robust algorithm for text detection in images." Image and Signal Processing and Analysis, 2003. ISPA 2003. Proceedings of the 3rd International Symposium on. Vol. 2. IEEE, 2003.
V.Vijayakumar,R.Nedunchezhianm(2011),”A Novel Method For Super Imposed Text Extraction In A Sports Video”,International Journal Of Computer Applications,Volume 15– No.1.
Trung Quy Phan ,Palaiahnakote ,Shivakumara Chew Lim Tan(2010),” A Skeleton-BasedMethod For Multi-Oriented Video Text Detection”, Das '10 Proceedings Of The 9th IAPR International Workshop On Document Analysis Systems,pp :271-278.
Gao, Xinbo, and Xiaoou Tang. "Unsupervised video-shot segmentation and model-free anchorperson detection for news video story parsing." IEEE Transactions on Circuits and Systems for Video Technology 12.9 (2002): 765-776.
J. Wang, T. Bolukbasi, K. Trapeznikov, and V. Saligrama, “Model selection by linear programming,” in ECCV, 2014.
G. Gkioxari, B. Hariharan, R. Girshick, and J. Malik, “Using k-poselets for detecting people and localizing their keypoints,” in CVPR, 2014.
X. Zeng, W. Ouyang, M. Wang, and X. Wang, “Deep learning of scenespecific classifier for pedestrian detection,” in ECCV, 2014.
B. Yang and R. Nevatia, “An online learned CRF model for multi-target tracking,” in CVPR, 2012
S. Zhang, A. Das, C. Ding, and A. K. Roy-Chowdhury, “Online social behavior modeling for multi-target tracking,” in CVPR Workshop, 2013.
X. Chen, Z. Qin, L. An, and B. Bhanu, “An online learned elementary grouping model for multi-target tracking,” in CVPR, 2014.
A. Dehghan, S. Modiri, and M. Shah, “GMMCP-Tracker: globally optimal generalized maximum multi clique problem for multiple object tracking,” in CVPR, 2015.
M. Harandi, C. Sanderson, C. Shen, and B. Lovell, “Dictionary learningand sparse coding on grassmann manifolds: an extrinsic solution,” in ICCV, 2013.
W. Nam, B. Han, and J. H. Han, “Local decorrelation for improved pedestrian detection,” in NIPS, 2014.
Refbacks
- There are currently no refbacks.
This work is licensed under a Creative Commons Attribution 3.0 License.