Deep Learning Methods for monitoring, detecting and measuring Deer Movements for Wildlife Conservation
Neelam Rawat1, J.S. Sodhi2, Rajesh K. Tyagi3
1Neelam Rawat*, Research Scholar, Amity University, Noida, India.
2J. S. Sodhi, Senior Vice President, Amity University. Noida, India.
3Rajesh K. Tyagi, Head & Professor, Computer Science, Amity University, Gurugram, India. 

Manuscript received on November 20, 2019. | Revised Manuscript received on November 26, 2019. | Manuscript published on 30 November, 2019. | PP: 3303-3308 | Volume-8 Issue-4, November 2019. | Retrieval Number: D8076118419/2019©BEIESP | DOI: 10.35940/ijrte.D8076.118419

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Abstract: Wildlife Researchers examine and dig video corpus for behavioral studies of free-ranging animals, which included monitoring, analyzing, classifying & detecting, managing, counting etc. Unfortunately, automated visual implementation for challenging real-time scenarios of wildlife is not an easy task especially for classification and recognition of wildlife-animals and estimate the sizes of wildlife populations. The aim of this paper is to bring state-of-the-art results from raw sensor data for learning features advancing automatic implementation and interpreting of animal movements from different perspectives. Also, turnout with an objectness score from object proposals generated by Region Proposal Network (RPN). The imagery data are captured from the motion sensor cameras and then through RCNN, Fast RCNN and Faster RCNN, it automatically are segmented and recognized the object with its objectness score. Conv Net automatically process these images and correctly recognizing the object. Experimentation results demonstrated prominent deer images with 96% accuracy with identifying three basic activities sleeping, grazing and resting. In addition, a measured implementation has been shown among CNN, RCNN, Fast RCNN and Faster RCNN.
Keywords: Wildlife, Ecosystem, Ecology, Deep Learning, Convolutional Neural Network, Region Proposal Network, Motion Sensor Cameras.
Scope of the Article: Deep Learning.