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

The entire system can be easily portable and it can be tested in any scenario. The following figure shows a brief overview of the system. The angles of the two cameras are adjusted to make the shooting range approximately the same, and the pixel values of the pictures are analysed by continuously taking pictures at the beginning to improve the detection algorithm. This process is a loop throughout the entire project.

New system assembly

The hardware is assembled into a closed box. The two cameras are on the left side of the box. They are fixed to the front of the box by blue fixing plates. The embedded circuit board is fixed in the middle of the box. The circuit board under the box is the power system. The power interface is used at the bottom of the box for external power supply equipment.

Design

Frame difference method

We first obtain the absolute value of the image brightness difference according to the difference between the images of the adjacent two frames, and then distinguish the motion area from the non-motion area according to the set threshold. The two-frame differential image is represented as: A_k (i,j)=|I_k (i,j)-I_(k-1) (i,j)| Where I_k (i,j) is the current frame pixel value and I_(k-1) (i,j) is the previous frame pixel value. When A_k (i,j)≤ T, the pixels of two adjacent frames do not change significantly, and it is determined that no animal has entered. When A_k (i,j)>T, when the adjacent two frame pixels change sufficiently, it is determined that there is an animal entering. The mathematical expression formula is C_k (i,j)={█(1 A_k (i,j)>T @0 A_k (i,j)≤T)┤ Where T is the detection threshold.