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Computational Agriculture: Video Monitoring of Honey Bees

Computational Agriculture: Video Monitoring of Honey Bees. Subhabrata Bhattacharya (University of Central Florida) , Lily Mummert, Jason Campbell, and Rahul Sukthankar (Intel Labs Pittsburgh). Video Monitoring/Challenges. Why honey bees?. Detection/Tracking Dataflow. Non-disruptive

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Computational Agriculture: Video Monitoring of Honey Bees

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  1. Computational Agriculture: Video Monitoring of Honey Bees Subhabrata Bhattacharya (University of Central Florida) , Lily Mummert, Jason Campbell, and Rahul Sukthankar (Intel Labs Pittsburgh) Video Monitoring/Challenges Why honey bees? Detection/Tracking Dataflow • Non-disruptive • Inexpensive • Leverages computer vision techniques • Commodity video (640x480 @ 30fps) Critical for Agricultural Pollination: ~130 crops (nuts, veggies), $15B in US Population in Decline : 50% decrease in managed colonies (2004) Preservation efforts are needed! Goal: Provide effective colony management information non-invasively (arrival, departure counts) • Clutter - leaves, debris • Lighting changesfrequently • Shadows look similar to bees • Changes in orientation • Perspective distortions Arrival/Departure Counts Input Video Filter Candidate blobs Analyze Tracks Match Blobs Detect Motion Moving blobs “Bee” Blobs Tracks Particular challenges of honey bee monitoring • Semi-stationary guard bees may confuse background techniques • Small size makes feature extraction difficult • Fast motion makes optical flow inaccurate • Visual similarity between bees makes track assignment difficult Algorithms Used • Motion Detection: Accumulative frame differencingcombined with pyramid background subtraction • Blob Filtering: Size-based thresholds, color histogram distance threshold (eliminate most shadows, leaves) • Tracking and Counting: Extract blob signature(area, orientation, eccentricity, distance from entry); perform greedy search; extrapolate tracks linearly to generate arrival/departure counts Results and Contributions • 94.1% detection @ 0.5 false alarms/frame(easy dataset) • 84.6% detection @ 2 false alarms/frame(intermediate dataset) • 72.0% true detections @ 8 false alarms/frame(hard dataset) • Multiple Object Tracking Precision Easy: 0.87, Intermediate: 0.79, Hard: 0.64 ground-truth-annotated datasets • Fast implementation (compiled, uses OpenCV) Better Better Better Better Better Better

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