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Semi-Automated Crow Detection System
Ricky Chan
Aaron Gupta
Michael Ma
Donny Sun
Agenda
- Introduction, Background and Project Description
- Hardware Assembly
- GUI (Graphical User Interface)
- Video Transmission
- Image Processing
- Testing and Results
- Future Work
- Difficulties and What We Have Learned
Introduction
The University of Washington, Bothell, Biology
Department is studying the crows and plants of the
North Creek wetlands.
Professor Doug Wacker is interested in studying the
roosting patterns of the crows.
Our project is integrating the quadcopter, which is
made by the mechanical engineering team, to help
Professor Doug Wacker to take images of crows.
Background
From fall to late spring, over 10,000 crows roost in
the North Creek wetlands at dusk.
Professor Wacker’s research includes the following
focuses:
(1) the number of crows that roost in the dusk;
(2) whether spatial patterns exist among individual
crow roosting locations; and
(3) how, if at all, the spatial roosting patterns relate
to the location of nearby plants.
Project Description
Video Transmission
Taking video stream from the quadcopter and Pi camera
Transferring video stream to ground station
Ground Station Computer and GUI
Run a script, download and save the video stream
User can capture images while video is playing
Image Processing
Run a MATLAB script to process the capture image
Hardware Assembly
WiFi Transmitter
Raspberry Pi Monitor ModuleRaspberry Pi ModuleRaspberry Pi NoIR Camera V2
WiFi Receiver
GUI
Language used: Python
Libraries used: PyGame & Tkinter (TeaKay Interface)
Platform: Unix/Linux
GUI
Quits program
Retrieves video from raspberry pi, converts into a usable format and opens video file
Opens video inside gui
Alternates playing and pausing current video
Skips 5 seconds ahead in video
Stops current video playing
Saves current frame as JPEG and processes it
Skips to a specific time frame
Captures and saves the images corresponding to the button clicks
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Dataflow Diagram
Video Transmission: System
Comprises of :- 2 dual band 2.4Ghz/5Ghz Wifi Adapters- 2 Raspberry Pi’s - Raspberry Pi Camera- USB
Utilizes:- Open source project - wifibroadcast
Inner workings:- Monitor mode & packet injection
R.Pi
Drone
Camera
Wifi adapter
R.Pi
Wifi adapter
USB R.Pi. Screen
live stream
Image Processing: Introduction
Original Image Processed Image
Image Processing: System Characteristics
Characteristic Limitation Reasoning
Crow Detection Detects, but doesn’t recognize, crows
Cannot differentiate between crow and bird-shaped objects
Sports Field is clear of debris; only crows roost on Sports Field
Background LocationUWB Sports Field Doesn’t work in North Creek
woods
Crows roost on Sports Field & S.F. has relatively low noise
Object Distance 5 - 15m Not guaranteed to work outside of this range
Meets contract specifications
Time of Day Dusk Untested at night Tested w/o infrared lights
Camera Perspective Top-down Top-down only Reduces noise, gimbal shape
Image Processing: Active Contour
- General theory
- Main Benefit
- Autonomous & adaptive method
- Drawbacks
- Minute features ignored
- Needs adjustment to increase accuracy
Image Processing: Flowchart
Image Processing: Example Case
1. Load captured screenshot 2. “Grayscale” image
Image Processing: Example Case (con’t.)
3. Adjust image contrast 4. Threshold image
Image Processing: Example Case (con’t.)
5. Trim image 6. Generate mask
Image Processing: Example Case (con’t.)
7. Apply mask to find crows 8. Determine background
Image Processing: Example Case (con’t.)
9. Subtract background 10. Generate final result
Testing/ResultsRange test for video transmission
1. 5 dBi antenna
Distance from soccer field through trees ~183m
Distance from soccer field through short foliage ~203m
2. 9 dBi antenna
Distance from soccer field through trees 237m~
Distance from soccer field through short foliage ~291m
Testing/Results (con’t.)
- Image tested : 13
- Failed Detection(s) : Image #8
- Total crows in 12 images: 57
- Total crows counted : 66
- FP : 66 - 57 = 9, TP: 57, FN = 0, TN = 0
- ≅ 86%
Image 1 2 3 4 5 6 7 8 9 10 11 12 13
# of crows
6 6 4 4 5 6 4 4 6 6 2 4 4
#s counted
6 6 4 6 5 6 6 30 8 5 3 6 5
Future Improvements
- Quadcopter
○ Reduce the operating noise of quadcopter
- Camera
○ Obtain and use high-resolution thermal vision camera
- GUI
○ Add more features (such as playback bar to control the video)
- Image Processing
○ Adapting machine learning algorithm to do the pattern recognition of crows
- Video transmission
○ Change system to 2.4GHz bandwidth to increase range and reliability
What We Have Learned
- The difficulties of working as a team
- Importance of self-motivation → Conducting individual research
- The Value of:
- Good communication skills
- Periodic re-evaluation of the project
Project Difficulties
- Broken quadcopter
- Evolution of project scope
- Hardware limitations
- Crows refused to cooperate
- Coordinating with external help
Thank You!
Questions?