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Wild Animal Detection from Highly Cluttered Forest Images Using Deep Residual Networks

  • Anamika Dhillon
  • Gyanendra K. VermaEmail author
Conference paper
Part of the Lecture Notes in Computer Science book series (LNCS, volume 11278)

Abstract

Wild animal detection is a dynamic research field since last decades. The videos acquired from camera-trap comprises of scenes that are cluttered that poses a challenge for detection of the wild animal. In this paper, we proposed a deep learning based system to detect wild animal from highly cluttered natural forest images. We have utilized Deep Residual Network (ResNet) for features extraction from cluttered forest images. These features are feed to classification through some of the best in class machine learning techniques, to be specific Support Vector Machine, K-Nearest Neighbor and Ensemble Tree. Our outcomes demonstrate that our detection system through ResNet outperforms compare to existing systems reported in the literature.

Keywords

Wild animal detection DCNN feature extractor Ensemble tree KNN Natural scenes SVM 

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Copyright information

© Springer Nature Switzerland AG 2018

Authors and Affiliations

  1. 1.Department of Computer EngineeringNational Institute of Technology KurukshetraKurukshetraIndia

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