Use of Computer Vision to Track Thin Body Motion with the Application of Tracking Passion Plant Vine Tendrils

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Virginia Tech

This research focuses on developing an algorithm set to track the vine tendril motion of a passiflora incarnate, commonly referred to as the passion fruit plant, to facilitate research into if there is a correlation between plant motion and plant health. An evaluation was done of clustering based color segmentation with a focus on K-means, feature / texture segmenta- tion utilizing Scale Invariant Feature Transforms (SIFT), and temporal based segmentation using Gaussian Mixture Model Background Subtraction to segment out the tendril in each video frame. Morphological image processing methods, such as dilation and connected com- ponent analysis, were used to clean up the segmentation results to give an estimate of the vine tendril’s location at each frame. Kalman filtering was then used to track the tendril’s location through the different frames dealing with large jumps in tendril location, cases where the tendril remained stationary between frames, and cases where there was error in the segmentation process. The resulting algorithm set was successful at tracking the tendril during times when the tendril had large jumps in position and it almost always succeeded in keeping track of the tendril during errors in the segmentation due to lack of tendril motion. The few cases that were not successful were evaluated and suggestions were made to resolve these issues in future data collection.

Computer Vision, Motion Tracking, Passion Plant, Vine Tendril, Kalman Filter