Tracking Migratory Birds Around Large Structures Presented by: Arik Brooks and Nicholas Patrick Advisors: Dr. Huggins, Dr. Schertz, and Dr. Stewart Senior Design Project Bradley University Department of Electrical and Computer Engineering

Project Background Every year, many birds are killed when their migration path takes them near tall structures on overcast nights. One widely accepted theory on why this happens is that the birds do not want to leave the lighted area near a structure and end up running into it. Wildlife biologists would like to study this phenomenon.

Outline Project summary Previous Work Detailed description System block diagram Subsystems Results

Outline Test Plan Datasheet Conclusions Suggestions for future work Questions

Project Summary The purpose of this project is to implement a system to track the flight paths of birds in real-time via stereoscopic imaging. The desired system output is a display depicting a 3-D representation of the trajectories of the birds, and data relating to the trajectories.

Previous Work 2003 seniors Brian Crombie and Matt Zivney Results: basic object position location in a laboratory environment with major limitations. The groundwork laid out in their project (algorithms, design equations, software organization, etc.) was used as a starting point for our system.

Detailed Description

System Block Diagram System

Hardware Block Diagram

Subsystems Cameras Frame Grabber PC’s/Network

Camera Subsystem Includes two cameras mounted in parallel a known distance apart allowing objects to be located in space. Inputs –Photons -- Images collected by the cameras –Synchronization -- Internal line lock Outputs –Data -- Image data transmitted to the frame grabber Operation in System –The cameras capture images at a rate dictated by the speed of the preprocessing algorithm

Frame Grabber Subsystem The frame grabber simultaneously captures images from both cameras and supplies the digitized image data to the PC. Inputs –Data -- Image data (NTSC format) from the cameras –Setup -- Information from the PC Outputs –Image Data to PC Operation in System –The frame grabber operates at a rate dictated by the speed of the preprocessing algorithm

PC’s/Network Subsystem Two PC’s are networked together to divide computation between the preprocessing and trajectory calculation computers. Inputs –Image Data -- Arrays of intensity information –Calibration Input -- Calibration data for the cameras being used Outputs –Display – GUI showing trajectories plotted in a three dimensional representation –Statistics -- Pertinent data calculated from bird trajectories –Raw Data -- Data file containing all preprocessed data Operation in System –The PC’s and network operate continuously

Results

Preprocessing Software

Streamlined Preprocessing in C++ Implement faster centroid location code. –Perimeter search vs. pixel-by-pixel search Improve background subtraction algorithm: –Fixed number of frames averaged for background to 256 –Current frame added using shift operations instead of multiplies/divides –Stored background is 16 bits: upper 8 bits are image data lower 8 bits for accumulating round-off error

Streamlined Preprocessing in C++ Improve background subtraction algorithm: –Speed Improvements (640x480, threshold image, do not find objects) Old Frames per Second New – 15.9 Frames per Second –Updating average every 60 frames Without find object function Frames per Second With find object function Frames per Second

Preprocessing in C++

Trajectory Determination in MATLAB Code to correlate objects between 2 cameras and over time restructured Added predictive searching to significantly improve tracking ability Improved graphing techniques real- time operation Implemented GUI for easy user interface

Trajectory Determination comparison - two tennis balls swinging System from last year:

Trajectory Determination comparison - two tennis balls swinging Current algorithm:

Trajectory Determination Software

Trajectory Determination in MATLAB Predictive Search Method –Search for a new point within a sphere defined by: Center at the location (x,y,z) predicted by the previous two points in the trajectory and the time taken between frame-grabs Radius determined by average bird velocity, time between frames, current velocity, and distance from the cameras

Trajectory Software GUI

Test Plan There will be three primary test procedures that will be performed to verify the system specifications: –Location Accuracy –Max/Min Distance from Cameras –Max # Objects Tennis Ball dispenser used in accuracy testing

Test Plan Location Accuracy –Capture data at known heights using a “stationary” object and balls dropped from the tennis ball dispenser. Compare theoretical to experimental. Max/Min Distance from Cameras –Repeat ‘Location Accuracy’ experiment at extremes of range. Max # Objects –Nerf Guns!!!

T-Bird Accuracy Test #1

T-Bird Accuracy Test #2

T-Bird Demonstration

Datasheet Average Migratory Bird Diameter: m Average Migratory Bird Speed: m/s Max # of Objects Tracked Simultaneously: TBD Max Distance from Cameras: 20 m Min Distance from Cameras: 3 m Max Location Error (theoretical): m Light Level Sensitivity: –Lab Cameras: 0.22 Lux –Low Light Cameras: Lux Max Framerate: ~15 FPS Total Volume of Space Observed: 606 m 3 Separation of Cameras assumed for calculations: 0.5 m

Conclusions Real-time tracking of multiple objects was achieved in a laboratory setting.

Suggestions for Future Work Implement boom (mechanical system and controls) Obtain and integrate high end cameras Optimize code (analyze algorithms, streamline processes) Port MATLAB to C Investigate feature detection methods for improved target recognition

Tracking Migratory Birds Around Large Structures Questions?