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Automated Testing and Detection of Dogs on Indian Roads


Affiliations
1 Shankersinh Vaghela Bapu Institute of Technology, Gandhinagar (Gujarat)
2 Sankalchand Patel College of Engineering, Visnagar (Gujarat), India
     

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Applications based on animal detection have a very important role in many real life situations. Some of these applications are detection and tracking of animals like elephant in forest for understanding their behavior with the environment, preventing animal vehicle collision on roads, preventing dangerous animal entering in residential area, and many more. In this paper first we will briefly summarize some of the methods used for detection of animals and then the application of our proposed method based on pattern matching mechanism using normalized cross correlation for identifying the animal. The proposed method has been applied for testing purpose to various images of dog. Simulation results show that our proposed method is efficient and the system has very low false positive and false negative rates. An overall efficiency of 86.25% is achieved for animal detection.

Keywords

Animal Detection, Image Processing, Frame Differencing, Normalized Cross Correlation, Pattern Matching.
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  • Automated Testing and Detection of Dogs on Indian Roads

Abstract Views: 239  |  PDF Views: 3

Authors

Sachin Sharma
Shankersinh Vaghela Bapu Institute of Technology, Gandhinagar (Gujarat)
D. J. Shah
Sankalchand Patel College of Engineering, Visnagar (Gujarat), India

Abstract


Applications based on animal detection have a very important role in many real life situations. Some of these applications are detection and tracking of animals like elephant in forest for understanding their behavior with the environment, preventing animal vehicle collision on roads, preventing dangerous animal entering in residential area, and many more. In this paper first we will briefly summarize some of the methods used for detection of animals and then the application of our proposed method based on pattern matching mechanism using normalized cross correlation for identifying the animal. The proposed method has been applied for testing purpose to various images of dog. Simulation results show that our proposed method is efficient and the system has very low false positive and false negative rates. An overall efficiency of 86.25% is achieved for animal detection.

Keywords


Animal Detection, Image Processing, Frame Differencing, Normalized Cross Correlation, Pattern Matching.