Adaboost Cascade Classifier for Classification and Identification of Wild Animals using Movidius Neural Compute Stick
S. Divya Meena1, L. Agilandeeswari2
1S. Divya Meen, Research Scholar, Department of Information Technology and Engineering, VIT University, Vellore (Tamil Nadu), India.
2Dr. L. Agilandeeswari, Associate Professor, Department of Information Technology and Engineering, VIT University, Vellore (Tamil Nadu), India.
Manuscript received on 18 December 2019 | Revised Manuscript received on 24 December 2019 | Manuscript Published on 31 December 2019 | PP: 495-499 | Volume-9 Issue-1S3 December 2019 | Retrieval Number: A10891291S319/19©BEIESP | DOI: 10.35940/ijeat.A1089.1291S319
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© The Authors. Blue Eyes Intelligence Engineering and Sciences Publication (BEIESP). This is an open access article under the CC-BY-NC-ND license (

Abstract: Deep learning has gone deeper and has been utilized in almost all the applications like object recognition, image classification, speech recognition and much more. Most of the real-time applications rely on deep learning for accurate results. But one downside to the deep learning is its demand for GPUs (Graphical Processing Unit) or TPUs (Tensor Processing Units) for faster execution. There was no one-stop-shop hardware and software for deep learning applications, until recently Intel launched the Movidius Neural Compute Stick (NCS). This sleek device provides the power of GPU in a CPU based system. In this work, we have modeled an animal detection system using NCS and AdaBoost classifier powered by Multi-Block Local Binary Pattern (MB-LBP) features. The model has been built upon AlexNet and has achieved an average accuracy of 96.8% and a false rate 2.3% in classifying the animals as wild and non-wild. Furthermore, the model has a speedup of 467% when compared to the execution in the CPU based system.
Keywords: Deep Neural Network, Alex Net, Movidius Neural Compute Stick, MP-LBP, AdaBoost Classifier.
Scope of the Article: Classification