Augmented Triplet Network for Individual Organism and Unique Object Classification for Reliable Monitoring of Ezoshika Deer

Wild Deer’s grazing in farmers and villager’s field has caused serious troubles for the residents and concerned authorities here in Northern Hokkaido of Japan. Furthermore, the number of deer in northern Japan is increasing rapidly leading to increment in road accidents which is also becoming a serious problem that needs to be handled. Several procedures have been followed to avoid or at least minimize the destruction caused by the white-tailed deer, but a solid solution is yet to be found. To this context, we are proposing a highly effective solutions to identify the individual wild deer and pinpoint the most trouble causing group of deer’s using augmented triplet network, a novel technique of computer vision. Initially the deer are recognized and classified individually which will help us track the deer and know the behavior of the deer in depth. Our research method identifies individual deer using several steps where we initially take video feed which is further divided into several frames. The extracted frames are then cleaned and processed for the training efficiency. The cleaned frames are then sent to the Convolution Network where the Nested triplet loss is calculated for the maximum accuracy.