Projects:2017s2-295 Feral Animal Detection using IR Thermal Imagery

Introduction

In Australia, especially in South Australia, numerous native species are killed by feral cats each year. In order to protect these animals and control the quantity of feral cats, it is necessary to design and build a system that can detect those cats, that is, automated sensor hardware image processing system, which could detect the local feral cat distribution. Image processing technology means that an image is analysed by a computer to achieve the desired result. This project is mainly to collect image data, and get on screening analysis of feral cats or other species for recording and distinguishing to simulate the situations in real world. It is worth mentioning that this is the second cycle of the project. This year, the project aims to rebuild the hardware and detection system, and process the resulting images to extract the shape of animals. At the end of this thesis, there are some conclusions after comparing with the initial dataset to find if the new system improves accuracy.

Objectives

This is the third year of the project, there is already an old system built by Richard & Yang, and we aim to redesign one improved process system in order to redevelop a new database. The images wanted are mostly concentrated at night, dusk, and dawn, then near IR camera and thermal camera can capture images of animal moving in front of the sensors. The several aims are as follows. Rebuild the hardware using improved devices Capture images and collect data as much as possible Test detection algorithms Do image processing to extract animals from background and give feedback whether the detection is effective Comparing the two systems, we may find whether the new one could improve the accuracy and speed.

Overview

1 Capture raw data from both IR and thermal cameras 2 Analyse raw data and find pictures of entire animals 3 Perform a simple analysis of these images to determine the initial thresholds and detection methods 4 Again capture raw data; use the selected thresholds for detection, testing the accuracy of the threshold selection 5 Repeat the fourth step several times to establish a more accurate detection, record and save these pictures 6 Start image processing (affine, linear correspondence, background subtraction, mask, etc.) to extract animals 7 Obtain results from step 6 Conclusions demonstrate the success rate and accuracy of detection 8 Make the above processes as a loop, and finally get the processed pictures to prepare for the future project