Projects - User contributions [en]

Projects:2021s1-13252 Feral Animal Recognition Using Thermal and Depth Sensing

2021-10-24T01:26:30Z

A1743107: Added everything. idk

[[Category:Projects]]
[[Category:Final Year Projects]]
[[Category:2021s1|13252]]
[[File:244396384 883589395914774 6192061019582818192 n.jpg|thumb|220px|Top View of hardware, including thermal and depth camera]]

== Abstract ==
A passive detection system to record feral animals was developed. A hardware demonstrator was developed to be deployed outside during nighttime to record the presence of any animal that moves in front of it. The system uses a thermal and depth camera to monitor for changes in temperature and distance and store these images in a dataset.

== Introduction ==
Each year feral animals cause significant harm in Australia, killing both native wildlife and damaging the environment. Common pest species in South Australia include feral cats, rabbits, and foxes, but also larger species such as camels and buffaloes cause significant damage in areas of northern Australia [1]. This research project is an extension of work conducted last year aimed at developing an automated feral animal detection system that could be deployed remotely to monitor for the presence of pest species. This system was based on a Raspberry PI board and made use of optical, thermal (long-wave infrared) and depth sensors to detect the presence of an animal. Machine learning techniques were then used to attempt to distinguish a few simple types (eg. dogs, cats humans etc). The aim of this follow-on project is to:
Build upon last year's project to improve detection capabilities and build a dataset for use in training a classification model
Develop a hardware demonstrator which can be deployed remotely overnight

=== Project team ===
==== Project students ====
* Benjamin Weichert
* Daniel Rohling
==== Supervisors ====
* Dr Danny Gibbins
* Dr Said Al-Sarawai
=== Objectives ===
1. Improve the detection capability in a way that minimises power consumption but allows it to operate more effectively between dusk and dawn in the absence of a light source.
2. Examine techniques in 3D sensing and machine learning that could be used to improve recognition.
3. Collect data and develop a hardware demonstrator that could be deployed in the field overnight.

== Method ==
=== Software ===
First, frames are extracted from each camera where detection programs decide if there is a warm, close subject in the image. The thermal and depth images containing the subject are mapped together and stored. Processing and inference programs then determine what, if any, animal is present.
[[File:Softwarediagram.png|thumb|center|400px|Diagram of software design]]

=== Hardware ===
A weatherproof box encloses the system so it can operate outside for long time periods. Depth and thermal cameras are connected to a Raspberry Pi which runs the software. The system can be powered using either an external 12V battery or a power adapter.
[[File:Hardwaredesign.png|thumb|center|400px|Diagram of hardware design]]

== Results ==
The images demonstrate the operation of the hardware demonstrator. The thermal camera images the dog using its temperature and the depth camera images the dog using its proximity.
[[File:Thermaldog.png|thumb|left|300px|Image of dog in thermal camera]] [[File:Depthdog.png|thumb|right|300px|Image of dog in depth camera]]

When deployed, the system can passively detect the presence of a warm subject, as shown in the graph to the right, by a statistically significant increase in the pixel values. If a detection occurs, that pixel is added to the binary detection mask, thereby isolating the warm subject from the background.
[[File:Thermaldetection.png|thumb|center|Results from the thermal camera, measuring thermal values against time. Clear detections can be seen]]

A similar approach is then taken with the depth camera where pixels with statistically close depth values are used to form a mask to fix a box around the subject and the real distance from the camera is used to calculate the subject’s height and width in centimetres.
The system developed provides a robust, energy-efficient detection system, combining thermal and depth imagery to facilitate nocturnal gathering of data and in turn, develop a machine-learning classification system.
[[File:Misty.png|thumb|center|Result of a detection using the depth camera. The dog is clearly detected and a bounding box is fixed around it]]

== Conclusion ==
In conclusion, the group was able to improve the detection and produce a system that could reliably detect and save data. The thermal and depth camera were successfully utilised and combined to produce a system that could operate outside, over long periods of time, and detect wild animals using both sensors. Data saved can be used by future students to train a classification model and determine the species of animals that are detected by the system.
== References ==
[1] "Feral Animals." Invasive Species Council. https://invasives.org.au/our-work/feral-animals/ (accessed 11/05/2021, 2021).

File:Hardwaredesign.png

2021-10-24T01:13:47Z

A1743107:

hardware diagram

File:Softwarediagram.png

2021-10-24T01:12:46Z

A1743107:

software diagram

File:Misty.png

2021-10-24T00:42:38Z

A1743107:

Depth camera successfully detects dog and fixes a bounding box around it

File:Thermaldetection.png

2021-10-24T00:42:02Z

A1743107:

Operation of thermal camera. Raw thermal value is recorded over time with clear detections present

File:Depthdog.png

2021-10-24T00:40:26Z

A1743107:

Depth Dog

File:Thermaldog.png

2021-10-24T00:40:03Z

A1743107:

Thermal dog

File:244396384 883589395914774 6192061019582818192 n.jpg

2021-10-24T00:34:37Z

A1743107:

Top view of system

Projects:2021s1-13252 Feral Animal Recognition Using Thermal and Depth Sensing

2021-04-13T00:42:56Z

A1743107:

[[Category:Projects]]
[[Category:Final Year Projects]]
[[Category:2021s1|13252]]
Each year feral animals cause significant harm in Australia, killing both native wildlife and damaging the environment. Common pest species in South Australia include feral cats, rabbits, and foxes, but also larger species such as camels and buffaloes cause significant damage in areas of northern Australia. This research project is an extension of work conducted last year aimed at developing an automated feral animal recognition system which could be deployed remotely to monitor for the presence of pest species. This system was based on a Raspberry PI board and made use of optical, thermal (long-wave infrared) and depth sensors to detect the presence of an animal. Machine learning techniques were then used to attempt to distinguish a few simple types (eg. dogs, cats humans etc). The aim of this follow-on project is to improve on the developed system in the following possible ways. 1. Improve the detection capability in a way that minimises power consumption but allows it to operate more effectively between dusk and dawn in the absence of a light source. 2. Examine techniques in 3D sensing and machine learning that could be used to improve recognition. 3. Collect data and develop a hardware demonstrator that could be deployed in the field overnight.
== Introduction ==
Project description here
Something about detecting feral animals, mostly cats, using thermal imagery and depth sensing

=== Project team ===
==== Project students ====
* Benjamin Weichert
* Daniel Rohling
==== Supervisors ====
* Danny Gibbins
* Said Al-Sarawai
==== Advisors ====
*
=== Objectives ===
Set of objectives

== Background ==
=== Topic 1 ===

== Method ==

== Results ==

== Conclusion ==

== References ==
[1] a, b, c, "Simple page", In Proceedings of the Conference of Simpleness, 2010.

[2] ...

Projects:2021s1-13252 Feral Animal Recognition Using Thermal and Depth Sensing

2021-04-12T11:37:25Z

A1743107:

[[Category:Projects]]
[[Category:Final Year Projects]]
[[Category:2021s1|13252]]
Each year feral animals cause significant harm in Australia, killing both native wildlife and damaging the environment. Common pest species in South Australia include feral cats, rabbits, and foxes, but also larger species such as camels and buffaloes cause significant damage in areas of northern Australia. This research project is an extension of work conducted last year aimed at developing an automated feral animal recognition system which could be deployed remotely to monitor for the presence of pest species. This system was based on a Raspberry PI board and made use of optical, thermal (long-wave infrared) and depth sensors to detect the presence of an animal. Machine learning techniques were then used to attempt to distinguish a few simple types (eg. dogs, cats humans etc). The aim of this follow-on project is to improve on the developed system in the following possible ways. 1. Improve the detection capability in a way that minimises power consumption but allows it to operate more effectively between dusk and dawn in the absence of a light source. 2. Examine techniques in 3D sensing and machine learning that could be used to improve recognition. 3. Collect data and develop a hardware demonstrator that could be deployed in the field overnight.
== Introduction ==
Project description here
Something about detecting feral animals, mostly cats, using thermal imagery and depth sensing

KFC will be used to capture feral animals [Source: https://www.yahoo.com/news/national-park-staff-luring-wildlife-005348039.html]

Leftover KFC will be consumed.

=== Project team ===
==== Project students ====
* Benjamin Weichert
* Daniel Rohling
==== Supervisors ====
* Danny Gibbins
* Said Al-Sarawai
==== Advisors ====
*
=== Objectives ===
Set of objectives

== Background ==
=== Topic 1 ===

== Method ==

== Results ==

== Conclusion ==

== References ==
[1] a, b, c, "Simple page", In Proceedings of the Conference of Simpleness, 2010.

[2] ...

Projects:2021s1-13252 Feral Animal Recognition Using Thermal and Depth Sensing

2021-04-12T11:37:03Z

A1743107: edited supervisors and Abstract from project description

[[Category:Projects]]
[[Category:Final Year Projects]]
[[Category:2021s1|13252]]
Each year feral animals cause significant harm in Australia, killing both native wildlife and damaging the environment. Common pest species in South Australia include feral cats, rabbits, and foxes, but also larger species such as camels and buffaloes cause significant damage in areas of northern Australia. This research project is an extension of work conducted last year aimed at developing an automated feral animal recognition system which could be deployed remotely to monitor for the presence of pest species. This system was based on a Raspberry PI board and made use of optical, thermal (long-wave infrared) and depth sensors to detect the presence of an animal. Machine learning techniques were then used to attempt to distinguish a few simple types (eg. dogs, cats humans etc). The aim of this follow-on project is to improve on the developed system in the following possible ways. 1. Improve the detection capability in a way that minimises power consumption but allows it to operate more effectively between dusk and dawn in the absence of a light source. 2. Examine techniques in 3D sensing and machine learning that could be used to improve recognition. 3. Collect data and develop a hardware demonstrator that could be deployed in the field overnight. IMPORTANT: Prior programming experience is required to work on this project. Some prior exposure to Raspberry PI boards (or similar), python etc would also be advantageous.
== Introduction ==
Project description here
Something about detecting feral animals, mostly cats, using thermal imagery and depth sensing

KFC will be used to capture feral animals [Source: https://www.yahoo.com/news/national-park-staff-luring-wildlife-005348039.html]

Leftover KFC will be consumed.

=== Project team ===
==== Project students ====
* Benjamin Weichert
* Daniel Rohling
==== Supervisors ====
* Danny Gibbins
* Said Al-Sarawai
==== Advisors ====
*
=== Objectives ===
Set of objectives

== Background ==
=== Topic 1 ===

== Method ==

== Results ==

== Conclusion ==

== References ==
[1] a, b, c, "Simple page", In Proceedings of the Conference of Simpleness, 2010.

[2] ...