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  Application of Hill Climbing Algorithm as Data Mining Technique for Surveillance of Real Time Video Streams  
  Authors : Avinash P. Ingle; Sushma D. Ghode
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Data mining is the application of statistical techniques and programmatic algorithms to discover previously unnoticed relationships within the data. Video surveillance has long been in use to monitor security sensitive areas such as banks, department stores, highways, crowded public places and borders. The advance in computing power, availability of large-capacity storage devices and high speed network infrastructure paved the way for cheaper, multi sensor video surveillance systems. The ultimate goal of the present generation surveillance systems is to allow video data to be used for on-line alarm generation to assist human operators and for offline inspection effectively. Moving object detection is the basic step for further analysis of video.


Published In : IJCSN Journal Volume 6, Issue 3

Date of Publication : June 2017

Pages : 356-361

Figures :06

Tables : --


Avinash P. Ingle : has completed his BE in IT from Government college of engineering Amaravti. He has completed his M.Tech in CSE from GHRCE Nagpur. He is a Assistant Professor at Department of Information Technology in Priydarshini Institute of Engineering and Technology, Nagpur. His research interests are Data Mining, Data Structure, Image Processing artificial intelligence.

Sushma D. Ghode : has completed her B.E. in Information Technology in 2005 and M.E. in Wireless Communication and Computing in 2010, both from GHRCE Nagpur, under RTM Nagpur University. She is pursuing PhD from dept. of Information Technology YCCE, Nagpur. She is presently working as assistant Professor in Dept. of Computer Science and Engineering at MIT Aurangabad (India).


Data Mining; Video surveillance; Object detection

The hill climbing algorithm will effectively find out the abnormal activities. It includes target area detection, video segmentation and clustering. Hill climbing can often produce a better result than other algorithms such purpose.


[1] Jinghua Wang and Guoyan Zhang, “Video Data Mining based on K-means Algorithm for Surveillance Video”, 2011 IEEE International Conference on data mining. [2] Jagannadan Varadarajan, Jean-Marc Odobez, R´emi Emonet “Multicamera Open Space Human Activity Discovery for Anomaly Detection”, 8th IEEE International Conference on Advanced Video and Signal-Based Surveillance, 2011. [3] Jun Wu; Zhitao Xiao,”Video surveillance object recognition based on shape and color features”, 3rd International conference on Image and Signal Processing (CISP), Publication Year: 2010, Page(s): 451 – 454. [4] Yanmin Luo; Minghong Liao; Zhipeng Zhan “A similarity analysis and clustering algorithm for video based on moving trajectory time series wavelet transform of moving object in Video” ,International Conference on Image Analysis and Signal Processing (IASP), Publication Year: 2010 , Page(s): 625 – 629 [5] Chakraborty, Ishita; Paul, Tanoy Kr.,“A Hybrid Clustering Algorithm for Fire Detection in Video and Analysis with Color Based Thresholding Method”, Publication Year: 2010 , Page(s): 277 – 280. [6] Ouivirach, K.; Dailey, M.N. “Clustering human behaviors with dynamic time warping and hidden Markov models for a video surveillance system”, International Conference on Electrical Engineering/Electronics Computer Telecommunications and Information Technology (ECTI-CON),Publication Year: 2010 , Page(s): 884 – 888. [7] Zhen Lei; Chao Wang; Qinghai Wang; Yanyan Huang “Real-Time Face Detection and Recognition for Video Surveillance Applications”, WRI World Congress on Computer Science and Information Engineering, , Publication Year: 2009 , Page(s): 168 – 172. [8] Singh, S.; Haiying Tu; Donat, W.; Pattipati, K.; Willett, P.,“Anomaly Detection via Feature-Aided Tracking and Hidden Markov Models”, IEEE Transactions on Systems, Man and Cybernetics, Part A: Systems and Humans,Publication Year: 2009 , Page(s): 144 – 159. [9] Chun-Man Mak; Wai-Kuen Cham,” Fast video object segmentation using Markov random field”, IEEE 10th Workshop on Multimedia Signal Processing, Publication Year: 2008, Page(s): 343 – 348. [10] Jungong Han; Minwei Feng; de With, P.H.N.,” A realtime video surveillance system with human occlusion handling using nonlinear regression”, Multimedia and Expo, 2008 IEEE International Conference on, Publication Year: 2008, Page(s): 305 – 308. [11] Swears, E.; Hoogs, A.; Perera, A.G.A.,” Learning Motion Patterns in Surveillance Video using HMM Clustering”, IEEE Workshop on Motion and video Computing, Publication Year: 2008 , Page(s): 1 – 8. [12] Fan Jiang; Ying Wu; Katsaggelos, A.K.,” Abnormal Event Detection from Surveillance Video by Dynamic Hierarchical Clustering”,IEEE International Conference on Image Processing, Publication Year: 2007 , Page(s): V - 145 - V – 148. [13] Jae Young Lee, William Hoff,” Activity Identification Utilizing Data Mining Techniques”, IEEE Workshop on Motion and Video Computing (WMVC'07). [14] Duarte Duque, Henrique Santos and Paulo Cortez,” Prediction of Abnormal Behaviors for Intelligent Video Surveillance Systems” ,IEEE Symposium on Computational Intelligence and Data Mining (CIDM 2007). [15] Kexue Dai; Guohui Li; Defeng Wu, “Motion clustering for similar video segments mining”,12th International Conference on Multi-Media Modeling Conference Proceedings, Publication Year: 2006. [16] Hua Zhong, Jianbo Shi, Mirk´o Visontai, “Detecting Unusual Activity in Video”, Proceedings of the 2004 IEEE Computer Society Conference on Computer Vision and Pattern Recognition (CVPR’04). [17] JungHwan Oh, Praveen Sankuratri, “Automatic distinction of camera and object motions In video sequences”, IEEE International Conference on Image Processing, Publication Year :2002. [18] Ismail Haritaoglu, David Harwood, Larry S. Davis,, “Real-Time Surveillance of People and Their Activities”, IEEE transactions on pattern analysis and machine intelligence, vol. 22, no. 8, August 2000. [19] AP Ingle, S Dongre “Surveillance of Real Time Video Streams by using Hill Climbing Algorithm”, International Journal of Computer Applications 65 (22), 2013. [20] S D Ghode, AP ingle, “A Survey On Nature Inspired Routing Algorithms For Ad Hoc Network” International Journal of Engineering Science and Research Technology, vol.2 issue 3, pp 404-409, March 2013.