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Computational Agriculture: Video Monitoring of Honey Bees Subhabrata Bhattacharya (Subh) Intel Mentors: L. Mummert R.

Computational Agriculture: Video Monitoring of Honey Bees Subhabrata Bhattacharya (Subh) Intel Mentors: L. Mummert R. Sukthankar J. Campbell. 07/08/2010. About Me. School : University of Central Florida Advisor(s) : Prof. Mubarak Shah, Dr. Rahul Sukthankar Thesis topic: Not decided yet

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Computational Agriculture: Video Monitoring of Honey Bees Subhabrata Bhattacharya (Subh) Intel Mentors: L. Mummert R.

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  1. Computational Agriculture: Video Monitoring of Honey Bees Subhabrata Bhattacharya (Subh)Intel Mentors: L. MummertR. SukthankarJ. Campbell 07/08/2010

  2. About Me • School : University of Central Florida • Advisor(s) : Prof. Mubarak Shah, Dr. Rahul Sukthankar • Thesis topic: Not decided yet • A little-known fact about me: I am an outdoor adventure enthusiast. 2

  3. Motivation • Bees are critical for agricultural pollination • ~130 crop, USD 15 bn in US in fruits, veggies, textiles, nuts etc. • Increasing pollination demands: almond 1.5M colonies by 2010, apples, plums … • Populations are in decline* • 1996 : feral bees virtually eliminated by varroa destructor • 2004: 50% decrease of managed colonies over past 50 yrs • 2006: cases of colony collapse disorder *USDA Agricultural research Service, 5/2008 3

  4. Provide colony information Stinging • Previous Approaches: • Manual Inspection • Photo-electrical counter [Sprangler et al.’69] • LASER Bar-code scanner [Sasaki et al.’89, Sandford et al.’89] • Infrared counter [Struye’94] • Mechanically tagged inspection [Landgraf et al.’07] Invasive Bees Entering Bees Exiting Tedious/ Impractical 4

  5. A cheaper alternative • Bee-hive monitoring using Computer vision [Campbell et al.08] • Non-invasive technique • Commodity sensor hardware (camera) 5

  6. The Research Question that I Hope to Answer “How to extend the video monitoring capabilities of the existing system to efficiently detect and track bees?” • Difficult: more shadow artifacts, occlusion, clutter, • Easy: Clean background, fewer bees, few shadow artifacts • Moderately difficult: cluttered background, more bees, shadow artifacts 6

  7. The Biggest Challenges in Answering this Question • Standard Computer Vision challenges • Clutter (moving foliage, debris) • Outdoor Illumination variation • Shadows • Orientation changes • Size changes (perspective effects) 7

  8. The Biggest Challenges in Answering this Question • Domain-specific computer vision challenges • Background subtraction difficult (guard bees) • Part based detectors fail (not enough shape info) • Blob matching difficulty (Similar Shape/color) • Optical flow only good for few pixels (here motion ~100px/frame) 100px 8

  9. My Approach to Answering this Question Input Video Arrival/Departure Counts Candidate blob Filtering Blob Matching Trajectory Analysis Motion Detection All moving blobs Filtered Blobs Trajectories 9

  10. Motion Based Detection • What methods we tried • GMM bg subtraction [Stauffer & Grimson, CVPR98] • Pixel Discrimination bg subtraction [Li et al., ACMMM03] • Pyramid based bg subtraction • Accumulative frame differencing (a.f.d) [Ali & Shah, SPIE06] • What method worked best • Pyramid bg subtraction (robust to large motion) + a.f.d (robust to local intensity changes) • 2 Pyramid levels for bg subtraction • 5 frame temporal window for a.f.d 10

  11. Candidate Blob Filtering • What methods we tried • Template matching [Campbell et al., VAIB08] • Viola-Jones face detector based cascade classifier trained on bee/non-bee patches [Viola & Jones, IJCV01] • Area/Size based thresholds • Train SVM classifier bee/non-bee pixels’ RGB intensity values • Matching color histogram of bee-pixels • What worked best • Size-based Threshold to filter small noisy blobs (usually shadows, leaves) • Compare histogram distances against threshold False Blobs True Blobs Bee Pixels non-Bee Pixels 11

  12. Blob Association/Obtaining Trajectories • What methods we tried • Particle filter based tracking – nonlinear state estimation of blobs • Blob Tracking – matching similar blobs • What worked best • Extract blob signature : Size, Eccentricity, Orientation, Displacement from entry • Assign blob using greedy search • Generate trajectories 12

  13. Qualitative Results of Trajectory Generation • Difficult: more shadow artifacts, occlusion, clutter • Easy: Clean background, fewer bees, few shadow artifacts • Moderately difficult: cluttered background, more bees, shadow artifacts 13

  14. Evaluation Metrics for Quantitative Analysis • Performance of detection • MODP - Multiple Object Detection Precision* • CPD - Cumulative Probability of Detection (Easy to visualize) • CFAR - Cumulative False Alarm Rate (Easy to visualize) • Performance of tracking • MOTP - Multiple Object Tracking Precision* *CLEAR MOT metrics [Bernardin,JIVP08 ] 14

  15. Analysis of CPD, CFAR on Easy Dataset Better Better • 94.19% True detections, 1 False positive every two frames • Color Histogram matching reduces number of False alarms • Only Bg Subtraction or only a.f.d techniques – not reliable 15

  16. Analysis of CPD, CFAR on Intermediate Dataset Better Better • 84.66% True detections, ~2 False positives every frame • 5-6 guard bees/frame (little motion), detection failed: Only appearance based detector (sliding window) might help 16

  17. Analysis of CPD, CFAR on Difficult Dataset Better Better • 72.00% True detections, ~8 False positives detected every frame • More guard bees/frame (little motion), detection failed • More clutter – blobs merge, bigger blobs filter out reducing true detections 17

  18. Normalized MODP for different methods a - Accumulative Frame Differencing [Ali & Shah, SPIE06] b - Background subtraction [Stauffer & Grimson, CVPR98] c - Background subtraction [Li et al., ACMMM03] d - Color SVM bee/non-bee classification e - Template matching (correlation) [Campbell et al., VAIB08] f -Haar features, cascade [Viola & Jones, IJCV01] g - Color Histogram, Bhattacharyya Distance Matching 18

  19. Preliminary Tracking Results • Normalized Multiple Object Tracking Precision (n-MOTP) • Fewer annotations for tracking (for the two harder datasets) *http://web.engr.oregonstate.edu/~hess/downloads/track.tar.gz 19

  20. Criteria for Success 75% Goal: • Comparative evaluation of the detection techniques (9 techniques tried) • Implement PF or HMM based tracker (PF tracker) 100% Goal: • Detection accuracy ~ 95% (Easy : 94.12%) • Counting (detection + tracking) accuracy ~ 90%* ( Needs improvement) 125% (aka Stretch) Goal: • High performance implementation (i.e. non-MATLAB) (C/OpenCV based detector/Tracker) • Determine the resources required to achieve real-time performance (NOT DONE) • * Extremely ambitious goal! 20

  21. Thank you! 21

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