Vision System for Wing Beat Analysis of Bats in the Wild 1 Boston University Department of Computer Science 2 Boston University Department of Biology Mikhail.

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Presentation transcript:

Vision System for Wing Beat Analysis of Bats in the Wild 1 Boston University Department of Computer Science 2 Boston University Department of Biology Mikhail Breslav 1, Nathan W. Fuller 2, and Margrit Betke 1 ComputerScience

Motivation Behavior Aerial Vehicles

Past Work Study Kinematics (Hubel. 2012) – Wind Tunnel – High Resolution Cameras 3D Tracking (Wu. 2009) – Outdoor environment – Model Bats as Points Behavior and Trajectory Analysis (Theriault. 2010, Fisher. 2010)

Goal Estimate Wing Beat Frequencies – Potential to improve tracking T Wing Beat Frequency: 1/T Hubel et al. 2012

Challenging Data Unpredictable Motions Relatively Low Resolution In FOV for a Short Time

Segmentation and Tracking Foreground / Background Estimation Kalman Filter

Shape-time Signals Output of Tracker Define “Shape”

Prototype Shapes Assumption – There are shapes that uniquely identify 3D poses for a given camera Example Currently chosen manually “up” “down” “neutral”

Main Idea A prototype shape is equal to a 3D pose Repeating prototype shapes in a shape-time signal Estimate Wing Beat

Shape Comparison Shape Distance – Shape Context Descriptor ( Belongie et al ) Invariant to translation, scale, and optionally rotation – Hungarian Algorithm Establish Correspondences Estimate Wing Relative to Body with feature W

Shape Similarity Scores Use Shape Distance and Ratio W to assign similarity score – Also consider the ‘none’ hypothesis ‘None’

“up” “down” “neutral” Process Shape-Time Signal Find confident matches to prototype shapes

“up” Process Shape-Time Signal “down” “neutral” Time Axis

Fast Fourier Transform Time Axis “up” “down” “neutral” FFT

Fast Fourier Transform Periodicity Estimate of 9.76 Hz

Experimental Results 20 Bats Both Automatic and Manual estimates

Discussion Reasonable Estimates – Deviates from manual annotations by 1.3 Hz on average, standard deviation 1.8 Hz – Falls within Hz as reported in biology literature (Foehring. 1984) Main Contribution – System for using shapes to estimate wing beat – First to do this for bats in the wild Vision based system

Future work Choosing prototype shapes – Automatically and Intelligently Understand the mapping between 2D shapes and 3D poses for a given model – Generalize across datasets Try more robust shape comparison measures

Thank You for your Attention!

Questions? Holding a bat!