Smart Surveillance Security Systems 4s for Detection using SIFT and SURF in Image Processing
Aghila Rajagopal1, R. T. Subhalakshmi2, Arunachalam3, D. Deepika4, N. Balaji5
1Dr. R. Aghila*, Department of Information Technology, Sethu Institute of Technology, Kariapatti. Tamilnadu, India.
2R. T. Subhalakshmi, Department of Information Technology, Sethu Institute of Technology, Kariapatti. Tamilnadu, India.
3Dr. M. Arunachalam, Professor, K.L.N .College of Information Technology, Pottapalaym,Sivagangai (Dt), Madurai, Tamilnadu, India.
4D. Deepika, Department of Computer Science and Technology, K.L.N .College of Information Technology, Pottapalaym, Sivagangai (Dt), Madurai. Tamilnadu, India.
5Dr. N. Balaji, Head, Department of Computer Science and Engineering in KPR Institute of Engineeirng and Technology, Coimabtore, India.
Manuscript received on January 26, 2020. | Revised Manuscript received on February 05, 2020. | Manuscript published on February 30, 2020. | PP: 2418-2420 | Volume-9 Issue-3, February 2020. | Retrieval Number: C5147029320/2020©BEIESP | DOI: 10.35940/ijeat.C5147.029320
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Abstract: Surveillance video is used for security purpose in our daily life in various places. It is used to observe the unusual activity that is taking place around us. Today in most of the shop owners have CCTV cameras to record, the uncertain activities and even it is used in houses in remote places. A system must be smart enough to detect. This paper uses SIFT and SURF algorithm for detection. Image registration is a development in which more than two images from various imaging equipment are reserved at various angles and at various times from the identical prospect and geometrically aligned for further exploration. Data may be from different sensors, CCTV taken at different times, depths, or perspective. Feature-DetectorDescriptor plays a vital role in feature matching application for selection of feature; this paper presents a comparative analysis of SIFT, SURF, algorithms. Experiments have been conducted on a wide range of images taken from datasets. A quantitative comparison is presented. This paper gives an useful ideas for making important decisions and it also helps in providing a smart security system.
Keywords: SIFT; SURF; image registration; nearest neighbor distance ratio; feature matching; scale invariance; rotation invariance; affine invariance; image matching; feature detection;