Identification and Enumeration of Waterfowl using Neural

Network Techniques

Michael Cash

ECE 539 Final Project

12/19/03

Background (i)

• Annual waterfowl surveys required for population estimations

• Many techniques used:– Area search (direct count)– Point count– Aerial Survey– Hunter seasonal survey

• Aerial surveying is costly but covers the most ground

Background (ii): Aerial Surveys

• Aerial pilots cruise along selected route, record number and species of birds seen

• Cost prohibits large number of pilots; small sample of actual population recorded

• Subject to human discretion and error

• Birds in flight are difficult to identify and count

Proposal

• Digital images can be processed to count the number of ducks in a flock

• One way to do this is with a k-means algorithm

• Once the locations of ducks in the image are known, a perceptron classifier could identify the species and/or gender

Image Pre-Processing

• Digital image of flock of ducks must reduce effects of background for clustering scheme to work

• Trees, vegetation removed by cropping (leave only sky and/or water)

• Median pixel value of entire image is set as nominal background

• Small range about nominal background set to provide for lighting & shadows

K-Means Clustering

• 250 randomly placed cluster centers

• Algorithm moves cluster centers until converged (centers no longer move) to <1E-30

• Empty clusters are removed

• Number of clusters remaining is number of ducks

Example Results

Cropped Image Feature Vectors (red)Cluster Centers (yellow)

59 observed birds 52 predicted birds

Example Results

Cropped Image Feature Vectors (red)Cluster Centers (yellow)

16 observed birds 25 predicted birds

Results Summary

• The algorithm produced results within 50%, but error can be reduced by:– Spacing birds (reducing clustered birds)– More pixels– Even background

Conclusion

• Proof of concept: one unaltered algorithm was used for many images, and produced desirable results for counting birds

• Perceptron classifier was not added due to lack of quality in images, but could be added in the future