Sandalwood is one of the most valuable trees in the world. Sandalwood robberies are increasing day by day as there isn't enough security being provided for them. Eventually they are getting one of the most endangered trees in our country. So, to protect the trees, in this paper proposed a method using inception-v3 model. The main objective is to detect if there is any person in the forest particularly in the restricted time. The importance of infrared images are that these can be visible even in the night time (i.e. even if no light). The main role is to create a classifier which detects the type of weapon hold by the person. This paper is based on Inception-v3 model of tensorflow. We use the technique transfer learning to retrain the infrared images. The implementation is done based on benchmark dataset OTCBVS under 'Terravic Weapon Infrared Database'. This paper propose a model with the 99% accuracy. © 2018 IEEE.

Naresh K

Department of Software Systems

School of Computer Science and Engineering

Vellore Campus

Mohan Sai S

Sai Ganesh M

Sai L

Nav A.

Vellore Institute of Technology (VIT) is a private university located in&nbsp;Tamil Nadu, India. Founded in 1984, as Vellore Engineering College, the institution offers 20 undergraduate, 34 postgraduate, four integrated and four research programs. It has campuses in Vellore, Amravati, Bhopal and Chennai.

VIT is one of the top ranked private universities in India according to NIRF, THE and QS Rankings.&nbsp;Govt. of India has recognized&nbsp;VIT, Vellore as an&nbsp;Institution of Eminence. This has allowed VIT to take independent quality initiatives and move up in world ranking.

&nbsp;

&nbsp;

VIT University

An Infrared Image Detecting System Model to Monitor Human with Weapon for Controlling Smuggling of Sandalwood Trees

2018 Second International Conference on Inventive Communication and Computational Technologies (ICICCT)

This paper proposes a simple and novel feature descriptor for face recognition called local diagonal extrema number pattern (LDENP). LDENP produces a compact code of facial features which is obtained by encoding the directional information of the face image. Further, LDENP micro-patterns are created using values and indices of the local diagonal extremas (i.e. minima and maxima) using first order local diagonal derivatives that extract the directional information. Moreover, the proposed algorithm partitions the face into several regions to facilitate extraction of features from each region individually. Consequently, the extracted features are concatenated into a single feature vector which is used as a face descriptor. In this work, only the diagonal neighbours are considered, hence, the dimension of the feature, and the computation time to recognize the face are reduced. Therefore, the curse of dimensionality problem is solved. Experimental results are carried out on standard benchmark databases like FERET, Extended YALE-B, ORL and LFW-a. Moreover, efficiency of LDENP descriptor is asserted by comparing recognition rates of the proposed method with other existing local-descriptor based methods. © 2017 Elsevier B.V.

Future Generation Computer Systems

Local diagonal extrema number pattern: A new feature descriptor for face recognition

Tropical Plant Research

Review on utilization of bark of red sanders (Pterocarpus santalinus L.): An economically important tree

Major Flowering Trees of Tropical Gardens

Sandalwood Tree

Arabian Journal for Science and Engineering

An Approach to Adaptive Pedestrian Detection and Classification in Infrared Images Based on Human Visual Mechanism and Support Vector Machine

2016 IEEE International Conference on Image Processing (ICIP)

Object classification in infrared images using deep representations

Journal	Data powered by Typeset2018 Second International Conference on Inventive Communication and Computational Technologies (ICICCT)
Publisher	Data powered by TypesetIEEE
Open Access	No